The document describes a proposed method for facial expression recognition in videos using 3D convolutional neural networks and long short-term memory. Specifically, it proposes a 3D Inception-ResNet architecture to extract both spatial and temporal features from video sequences. It also incorporates facial landmarks to emphasize important facial components. The landmarks are used to generate filters during training. Finally, an LSTM unit is used to further extract temporal information from the enhanced feature maps output by the 3D Inception-ResNet layers. The proposed method is evaluated on several facial expression databases and is shown to outperform state-of-the-art methods.
Facial Emotion Recognition using Convolution Neural NetworkYogeshIJTSRD
Facial expression plays a major role in every aspect of human life for communication. It has been a boon for the research in facial emotion with the systems that give rise to the terminology of human computer interaction in real life. Humans socially interact with each other via emotions. In this research paper, we have proposed an approach of building a system that recognizes facial emotion using a Convolutional Neural Network CNN which is one of the most popular Neural Network available. It is said to be a pattern recognition Neural Network. Convolutional Neural Network reduces the dimension for large resolution images and not losing the quality and giving a prediction output whats expected and capturing of the facial expressions even in odd angles makes it stand different from other models also i.e. it works well for non frontal images. But unfortunately, CNN based detector is computationally heavy and is a challenge for using CNN for a video as an input. We will implement a facial emotion recognition system using a Convolutional Neural Network using a dataset. Our system will predict the output based on the input given to it. This system can be useful for sentimental analysis, can be used for clinical practices, can be useful for getting a persons review on a certain product, and many more. Raheena Bagwan | Sakshi Chintawar | Komal Dhapudkar | Alisha Balamwar | Prof. Sandeep Gore "Facial Emotion Recognition using Convolution Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39972.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/39972/facial-emotion-recognition-using-convolution-neural-network/raheena-bagwan
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
We seek to classify images into different emotions using a first 'intuitive' machine learning approach, then training models using convolutional neural networks and finally using a pretrained model for better accuracy.
This document describes a project to build a convolutional neural network (CNN) model to recognize six basic human emotions (angry, fear, happy, sad, surprise, neutral) from facial expressions. The CNN architecture includes convolutional, max pooling and fully connected layers. Models are trained on two datasets - FERC and RaFD. Experimental results show that Model C achieves the best testing accuracy of 71.15% on FERC and 63.34% on RaFD. Visualizations of activation maps and a prediction matrix are provided to analyze the model's performance and confusions between emotions. A live demo application is also developed using OpenCV to demonstrate real-time emotion recognition from video frames.
This document presents a hybrid framework for facial expression recognition that uses SVD, PCA, and SURF. It extracts features using PCA with SVD, classifies expressions with an SVM classifier, and performs emotion detection with regression and SURF features. The framework achieves 98.79% accuracy and 67.79% average recognition on a database of 50 images with 5 expressions. It provides a concise facial expression recognition system using a combination of dimensionality reduction, classification, and feature detection techniques.
Automatic Emotion Recognition Using Facial Expression: A ReviewIRJET Journal
This document reviews automatic emotion recognition using facial expressions. It discusses how facial expressions are an important form of non-verbal communication that can express human perspectives and mental states. The document then summarizes several popular techniques for automatic facial expression recognition systems, including those based on statistical movement, auto-illumination correction, identification-driven emotion recognition for social robots, e-learning approaches, cognitive analysis for interactive TV, and motion detection using optical flow. Each technique is assessed in terms of its pros and cons. The goal of the techniques is to enhance human-computer interaction through more accurate and real-time recognition of facial expressions and emotions.
Facial Emotion Recognition using Convolution Neural NetworkYogeshIJTSRD
Facial expression plays a major role in every aspect of human life for communication. It has been a boon for the research in facial emotion with the systems that give rise to the terminology of human computer interaction in real life. Humans socially interact with each other via emotions. In this research paper, we have proposed an approach of building a system that recognizes facial emotion using a Convolutional Neural Network CNN which is one of the most popular Neural Network available. It is said to be a pattern recognition Neural Network. Convolutional Neural Network reduces the dimension for large resolution images and not losing the quality and giving a prediction output whats expected and capturing of the facial expressions even in odd angles makes it stand different from other models also i.e. it works well for non frontal images. But unfortunately, CNN based detector is computationally heavy and is a challenge for using CNN for a video as an input. We will implement a facial emotion recognition system using a Convolutional Neural Network using a dataset. Our system will predict the output based on the input given to it. This system can be useful for sentimental analysis, can be used for clinical practices, can be useful for getting a persons review on a certain product, and many more. Raheena Bagwan | Sakshi Chintawar | Komal Dhapudkar | Alisha Balamwar | Prof. Sandeep Gore "Facial Emotion Recognition using Convolution Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39972.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/39972/facial-emotion-recognition-using-convolution-neural-network/raheena-bagwan
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
We seek to classify images into different emotions using a first 'intuitive' machine learning approach, then training models using convolutional neural networks and finally using a pretrained model for better accuracy.
This document describes a project to build a convolutional neural network (CNN) model to recognize six basic human emotions (angry, fear, happy, sad, surprise, neutral) from facial expressions. The CNN architecture includes convolutional, max pooling and fully connected layers. Models are trained on two datasets - FERC and RaFD. Experimental results show that Model C achieves the best testing accuracy of 71.15% on FERC and 63.34% on RaFD. Visualizations of activation maps and a prediction matrix are provided to analyze the model's performance and confusions between emotions. A live demo application is also developed using OpenCV to demonstrate real-time emotion recognition from video frames.
This document presents a hybrid framework for facial expression recognition that uses SVD, PCA, and SURF. It extracts features using PCA with SVD, classifies expressions with an SVM classifier, and performs emotion detection with regression and SURF features. The framework achieves 98.79% accuracy and 67.79% average recognition on a database of 50 images with 5 expressions. It provides a concise facial expression recognition system using a combination of dimensionality reduction, classification, and feature detection techniques.
Automatic Emotion Recognition Using Facial Expression: A ReviewIRJET Journal
This document reviews automatic emotion recognition using facial expressions. It discusses how facial expressions are an important form of non-verbal communication that can express human perspectives and mental states. The document then summarizes several popular techniques for automatic facial expression recognition systems, including those based on statistical movement, auto-illumination correction, identification-driven emotion recognition for social robots, e-learning approaches, cognitive analysis for interactive TV, and motion detection using optical flow. Each technique is assessed in terms of its pros and cons. The goal of the techniques is to enhance human-computer interaction through more accurate and real-time recognition of facial expressions and emotions.
IRJET- Facial Emotion Detection using Convolutional Neural NetworkIRJET Journal
This document describes a system for facial emotion detection using convolutional neural networks. The system uses Haar cascade classifiers to detect faces in images and then applies a convolutional neural network to recognize seven basic emotions (happiness, sadness, anger, fear, disgust, surprise, contempt) from facial expressions. The convolutional neural network architecture includes convolutional layers to extract features, ReLU layers for non-linearity, pooling layers for dimensionality reduction, and fully connected layers for emotion classification. The system is described as having potential applications in security systems, driver monitoring systems, and other real-time emotion detection use cases.
A deep learning facial expression recognition based scoring system for restau...CloudTechnologies
A deep learning facial expression recognition based scoring system for restaurants
Cloud Technologies providing Complete Solution for all
Academic Projects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Email ID: cloudtechnologiesprojects@gmail.com
Facial expression recongnition Techniques, Database and Classifiers Rupinder Saini
This document discusses various techniques for facial expression recognition including eigenface approach, principal component analysis (PCA), Gabor wavelets, PCA with singular value decomposition, independent component analysis with PCA, local Gabor binary patterns, and support vector machines. It describes databases commonly used for facial expression recognition research and classifiers such as Euclidean distance, backpropagation neural networks, PCA, and linear discriminant analysis. The document concludes that combining multiple techniques can achieve more accurate facial expression recognition compared to individual techniques alone by extracting relevant features and evaluating results.
This document describes various algorithms used to build a facial emotion recognition system, including Haar cascade, HOG, Eigenfaces, and Fisherfaces. It explains how each algorithm works, such as how Haar cascade detects facial features and HOG extracts histograms of gradients. The system is trained on the CK+ dataset and uses Eigenface and Fisherface classifiers to classify emotions, achieving higher accuracy (86.54%) with Fisherfaces. It provides code snippets of key steps like cropping, resizing images, splitting data, and predicting emotions.
Predicting Emotions through Facial Expressions twinkle singh
This document describes a facial expression recognition system with two parts: face recognition and facial expression recognition. It discusses using principal component analysis (PCA) and linear discriminative analysis (LDA) for face recognition, and PCA to extract eigenfaces for facial expression recognition. The system first performs face detection, then extracts facial expression data and classifies the expression. MATLAB is used as the tool for its faster programming capabilities.
Facial expression recognition using pca and gabor with jaffe database 11748EditorIJAERD
This document discusses a facial expression recognition system that uses two different feature extraction methods - Principal Component Analysis (PCA) and Gabor filters - with the JAFFE facial expression database. PCA is used to reduce the dimensionality of the feature space, while Gabor filters are used to extract features due to their ability to encode spatial frequency and orientation information. The system that uses Gabor filters and PCA achieved better accuracy than one that used only PCA. The document provides mathematical background on PCA and Gabor filters and describes the steps of the facial expression recognition algorithm.
Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
This document provides a literature review of various techniques for automatic facial expression recognition. It discusses approaches such as principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA), 2D PCA, global eigen approaches using color images, subpattern extended 2D PCA, multilinear image analysis, color subspace LDA, 2D Gabor filter banks, and local Gabor binary patterns. It provides a table comparing the performance and disadvantages of these different methods. Recently, tensor perceptual color frameworks have been introduced that apply tensor concepts and perceptual color spaces to improve recognition performance under varying illumination conditions.
Facial expression recognition based on image featureTasnim Tara
This document presents a method for facial expression recognition based on image features. It discusses existing works that use techniques like PCA and Gabor wavelets for feature extraction and Euclidean distance for classification. The proposed method uses Gaussian filtering, radial symmetry transform, and edge projection for feature extraction, and calculates a feature vector based on geometric facial parameters to classify expressions using Euclidean distance. It aims to recognize six basic expressions accurately from the JAFFE database and could be developed for real-time video recognition in the future.
This document presents a method for real-time facial expression analysis using principal component analysis (PCA). The method involves detecting faces, extracting expression features from the eye and mouth regions, applying PCA to extract texture features, and using a support vector machine classifier to classify expressions. The proposed approach was tested on a database of facial images with expressions categorized as happy, angry, disgust, sad, or neutral. PCA was used to select the most relevant eigenfaces and reduce the dimensionality of the feature space for more efficient classification of expressions in real-time.
This document provides a synopsis for a project on emotion detection from facial expressions. It outlines the objectives to develop an automatic emotion detection system using machine learning algorithms to analyze facial expressions in video frames and compare them to a database to classify emotions. The technical details discuss using a facial tracker and extracting features to represent expressions. Classification algorithms like KNN, SVM, and voting will be used for recognition and mapping expressions to emotions. Future work may include 3D processing, speech recognition, and detecting micro-expressions.
Emotion recognition and drowsiness detection using python.pptGopi Naidu
This document proposes a system that uses artificial intelligence and digital image processing techniques for facial recognition, emotion recognition, drowsiness detection, and ID card detection. It describes the key components of the system including facial recognition using kNN, emotion recognition using CNNs, drowsiness detection by analyzing eye blinking, and ID card detection through shape and color detection. The overall goal of the project is to build an affordable and efficient surveillance and feedback system using these computer vision and AI techniques.
IRJET- Emotion Classification of Human Face Expressions using Transfer Le...IRJET Journal
This document discusses emotion classification of human facial expressions using transfer learning. It presents a literature review of existing works on emotion detection from facial images. The proposed system aims to reduce computational time for model training by using a pre-trained convolutional neural network model and fine-tuning it on a new dataset rather than training from scratch. The system first extracts bottleneck features from the pre-trained model, retrains on new data, then combines the pre-trained and retrained models for emotion classification. This allows it to leverage existing knowledge from vast labeled datasets and focus learning on new dataset aspects.
This document discusses face detection and recognition techniques using MATLAB. It begins with an abstract describing face detection as determining the location and size of faces in images and ignoring other objects. It then discusses implementing an algorithm to recognize faces from images in near real-time by calculating the difference between an input face and the average of faces in a training set. The document then provides details on various face recognition methods, the 5 step process of facial recognition, benefits and applications, and concludes that recent algorithms are much more accurate than older ones.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
The document describes a deep learning model called Deep-Emotion that uses an attentional convolutional network for facial expression recognition. The model focuses on important facial regions like the eyes and mouth that are most relevant to detecting emotions. The model achieves state-of-the-art accuracy on several facial expression datasets, outperforming previous methods. Visualization techniques show the model learns to focus on different facial regions for different emotions.
Implementation of Face Recognition in Cloud Vision Using Eigen FacesIJERA Editor
Cloud computing comes in several different forms and this article documents how service, Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The papers discuss a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed System is connection of two stages – Feature extraction using principle component analysis and recognition using the back propagation Network. This paper also discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The dispute lies with how to performance task partitioning from mobile devices to cloud and distribute compute load among cloud servers to minimize the response time given diverse communication latencies and server compute powers
This document outlines a research project on designing an automatic system to distinguish facial expressions. It presents an introduction discussing the importance and challenges of facial expression recognition. It provides an outline of the proposed system including aims to use programming for design and implementation. It discusses the basic structure of facial expression analysis and concludes the objective is to analyze facial expressions through steps like feature extraction and expression classification.
This document discusses using a deep neural network with vectorized facial features for emotion recognition. It introduces a novel method of vectorizing facial landmarks extracted from images to represent facial expressions. A deep neural network is trained on the vectorized facial feature data to classify emotions. The network achieves 84.33% accuracy on an emotion recognition test set. Compared to convolutional neural networks, the proposed deep neural network requires less training time and data.
FACE EXPRESSION RECOGNITION USING CONVOLUTION NEURAL NETWORK (CNN) MODELS ijgca
This paper proposes the design of a Facial Expression Recognition (FER) system based on deep
convolutional neural network by using three model. In this work, a simple solution for facial expression
recognition that uses a combination of algorithms for face detection, feature extraction and classification
is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models
are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended
Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this
study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that
AlexNet model achieved the best accuracy (88.2%) compared to other models.
Robust face recognition using convolutional neural networks combined with Kr...IJECEIAES
Face recognition is a challenging task due to the complexity of pose variations, occlusion and the variety of face expressions performed by distinct subjects. Thus, many features have been proposed, however each feature has its own drawbacks. Therefore, in this paper, we propose a robust model called Krawtchouk moments convolutional neural networks (KMCNN) for face recognition. Our model is divided into two main steps. Firstly, we use 2D discrete orthogonal Krawtchouk moments to represent features. Then, we fed it into convolutional neural networks (CNN) for classification. The main goal of the proposed approach is to improve the classification accuracy of noisy grayscale face images. In fact, Krawtchouk moments are less sensitive to noisy effects. Moreover, they can extract pertinent features from an image using only low orders. To investigate the robustness of the proposed approach, two types of noise (salt and pepper and speckle) are added to three datasets (YaleB extended, our database of faces (ORL), and a subset of labeled faces in the wild (LFW)). Experimental results show that KMCNN is flexible and performs significantly better than using just CNN or when we combine it with other discrete moments such as Tchebichef, Hahn, Racah moments in most densities of noises.
IRJET- Facial Emotion Detection using Convolutional Neural NetworkIRJET Journal
This document describes a system for facial emotion detection using convolutional neural networks. The system uses Haar cascade classifiers to detect faces in images and then applies a convolutional neural network to recognize seven basic emotions (happiness, sadness, anger, fear, disgust, surprise, contempt) from facial expressions. The convolutional neural network architecture includes convolutional layers to extract features, ReLU layers for non-linearity, pooling layers for dimensionality reduction, and fully connected layers for emotion classification. The system is described as having potential applications in security systems, driver monitoring systems, and other real-time emotion detection use cases.
A deep learning facial expression recognition based scoring system for restau...CloudTechnologies
A deep learning facial expression recognition based scoring system for restaurants
Cloud Technologies providing Complete Solution for all
Academic Projects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Email ID: cloudtechnologiesprojects@gmail.com
Facial expression recongnition Techniques, Database and Classifiers Rupinder Saini
This document discusses various techniques for facial expression recognition including eigenface approach, principal component analysis (PCA), Gabor wavelets, PCA with singular value decomposition, independent component analysis with PCA, local Gabor binary patterns, and support vector machines. It describes databases commonly used for facial expression recognition research and classifiers such as Euclidean distance, backpropagation neural networks, PCA, and linear discriminant analysis. The document concludes that combining multiple techniques can achieve more accurate facial expression recognition compared to individual techniques alone by extracting relevant features and evaluating results.
This document describes various algorithms used to build a facial emotion recognition system, including Haar cascade, HOG, Eigenfaces, and Fisherfaces. It explains how each algorithm works, such as how Haar cascade detects facial features and HOG extracts histograms of gradients. The system is trained on the CK+ dataset and uses Eigenface and Fisherface classifiers to classify emotions, achieving higher accuracy (86.54%) with Fisherfaces. It provides code snippets of key steps like cropping, resizing images, splitting data, and predicting emotions.
Predicting Emotions through Facial Expressions twinkle singh
This document describes a facial expression recognition system with two parts: face recognition and facial expression recognition. It discusses using principal component analysis (PCA) and linear discriminative analysis (LDA) for face recognition, and PCA to extract eigenfaces for facial expression recognition. The system first performs face detection, then extracts facial expression data and classifies the expression. MATLAB is used as the tool for its faster programming capabilities.
Facial expression recognition using pca and gabor with jaffe database 11748EditorIJAERD
This document discusses a facial expression recognition system that uses two different feature extraction methods - Principal Component Analysis (PCA) and Gabor filters - with the JAFFE facial expression database. PCA is used to reduce the dimensionality of the feature space, while Gabor filters are used to extract features due to their ability to encode spatial frequency and orientation information. The system that uses Gabor filters and PCA achieved better accuracy than one that used only PCA. The document provides mathematical background on PCA and Gabor filters and describes the steps of the facial expression recognition algorithm.
Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
This document provides a literature review of various techniques for automatic facial expression recognition. It discusses approaches such as principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA), 2D PCA, global eigen approaches using color images, subpattern extended 2D PCA, multilinear image analysis, color subspace LDA, 2D Gabor filter banks, and local Gabor binary patterns. It provides a table comparing the performance and disadvantages of these different methods. Recently, tensor perceptual color frameworks have been introduced that apply tensor concepts and perceptual color spaces to improve recognition performance under varying illumination conditions.
Facial expression recognition based on image featureTasnim Tara
This document presents a method for facial expression recognition based on image features. It discusses existing works that use techniques like PCA and Gabor wavelets for feature extraction and Euclidean distance for classification. The proposed method uses Gaussian filtering, radial symmetry transform, and edge projection for feature extraction, and calculates a feature vector based on geometric facial parameters to classify expressions using Euclidean distance. It aims to recognize six basic expressions accurately from the JAFFE database and could be developed for real-time video recognition in the future.
This document presents a method for real-time facial expression analysis using principal component analysis (PCA). The method involves detecting faces, extracting expression features from the eye and mouth regions, applying PCA to extract texture features, and using a support vector machine classifier to classify expressions. The proposed approach was tested on a database of facial images with expressions categorized as happy, angry, disgust, sad, or neutral. PCA was used to select the most relevant eigenfaces and reduce the dimensionality of the feature space for more efficient classification of expressions in real-time.
This document provides a synopsis for a project on emotion detection from facial expressions. It outlines the objectives to develop an automatic emotion detection system using machine learning algorithms to analyze facial expressions in video frames and compare them to a database to classify emotions. The technical details discuss using a facial tracker and extracting features to represent expressions. Classification algorithms like KNN, SVM, and voting will be used for recognition and mapping expressions to emotions. Future work may include 3D processing, speech recognition, and detecting micro-expressions.
Emotion recognition and drowsiness detection using python.pptGopi Naidu
This document proposes a system that uses artificial intelligence and digital image processing techniques for facial recognition, emotion recognition, drowsiness detection, and ID card detection. It describes the key components of the system including facial recognition using kNN, emotion recognition using CNNs, drowsiness detection by analyzing eye blinking, and ID card detection through shape and color detection. The overall goal of the project is to build an affordable and efficient surveillance and feedback system using these computer vision and AI techniques.
IRJET- Emotion Classification of Human Face Expressions using Transfer Le...IRJET Journal
This document discusses emotion classification of human facial expressions using transfer learning. It presents a literature review of existing works on emotion detection from facial images. The proposed system aims to reduce computational time for model training by using a pre-trained convolutional neural network model and fine-tuning it on a new dataset rather than training from scratch. The system first extracts bottleneck features from the pre-trained model, retrains on new data, then combines the pre-trained and retrained models for emotion classification. This allows it to leverage existing knowledge from vast labeled datasets and focus learning on new dataset aspects.
This document discusses face detection and recognition techniques using MATLAB. It begins with an abstract describing face detection as determining the location and size of faces in images and ignoring other objects. It then discusses implementing an algorithm to recognize faces from images in near real-time by calculating the difference between an input face and the average of faces in a training set. The document then provides details on various face recognition methods, the 5 step process of facial recognition, benefits and applications, and concludes that recent algorithms are much more accurate than older ones.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
The document describes a deep learning model called Deep-Emotion that uses an attentional convolutional network for facial expression recognition. The model focuses on important facial regions like the eyes and mouth that are most relevant to detecting emotions. The model achieves state-of-the-art accuracy on several facial expression datasets, outperforming previous methods. Visualization techniques show the model learns to focus on different facial regions for different emotions.
Implementation of Face Recognition in Cloud Vision Using Eigen FacesIJERA Editor
Cloud computing comes in several different forms and this article documents how service, Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The papers discuss a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed System is connection of two stages – Feature extraction using principle component analysis and recognition using the back propagation Network. This paper also discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The dispute lies with how to performance task partitioning from mobile devices to cloud and distribute compute load among cloud servers to minimize the response time given diverse communication latencies and server compute powers
This document outlines a research project on designing an automatic system to distinguish facial expressions. It presents an introduction discussing the importance and challenges of facial expression recognition. It provides an outline of the proposed system including aims to use programming for design and implementation. It discusses the basic structure of facial expression analysis and concludes the objective is to analyze facial expressions through steps like feature extraction and expression classification.
This document discusses using a deep neural network with vectorized facial features for emotion recognition. It introduces a novel method of vectorizing facial landmarks extracted from images to represent facial expressions. A deep neural network is trained on the vectorized facial feature data to classify emotions. The network achieves 84.33% accuracy on an emotion recognition test set. Compared to convolutional neural networks, the proposed deep neural network requires less training time and data.
FACE EXPRESSION RECOGNITION USING CONVOLUTION NEURAL NETWORK (CNN) MODELS ijgca
This paper proposes the design of a Facial Expression Recognition (FER) system based on deep
convolutional neural network by using three model. In this work, a simple solution for facial expression
recognition that uses a combination of algorithms for face detection, feature extraction and classification
is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models
are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended
Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this
study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that
AlexNet model achieved the best accuracy (88.2%) compared to other models.
Robust face recognition using convolutional neural networks combined with Kr...IJECEIAES
Face recognition is a challenging task due to the complexity of pose variations, occlusion and the variety of face expressions performed by distinct subjects. Thus, many features have been proposed, however each feature has its own drawbacks. Therefore, in this paper, we propose a robust model called Krawtchouk moments convolutional neural networks (KMCNN) for face recognition. Our model is divided into two main steps. Firstly, we use 2D discrete orthogonal Krawtchouk moments to represent features. Then, we fed it into convolutional neural networks (CNN) for classification. The main goal of the proposed approach is to improve the classification accuracy of noisy grayscale face images. In fact, Krawtchouk moments are less sensitive to noisy effects. Moreover, they can extract pertinent features from an image using only low orders. To investigate the robustness of the proposed approach, two types of noise (salt and pepper and speckle) are added to three datasets (YaleB extended, our database of faces (ORL), and a subset of labeled faces in the wild (LFW)). Experimental results show that KMCNN is flexible and performs significantly better than using just CNN or when we combine it with other discrete moments such as Tchebichef, Hahn, Racah moments in most densities of noises.
Facial recognition based on enhanced neural networkIAESIJAI
Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
The performance of various acoustic feature extraction methods has been compared in this work using
Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic
features are a series of vectors that represents the speech signals. They can be classified in either words or
sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic
vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector
extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction
(PLP) have also been used. These two methods closely resemble the human auditory system. These feature
vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes
are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to
investigate the nature of those acoustic features.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
The performance of various acoustic feature extraction methods has been compared in this work using
Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic
features are a series of vectors that represents the speech signals. They can be classified in either words or
sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic
vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector
extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction
(PLP) have also been used. These two methods closely resemble the human auditory system. These feature
vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes
are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to
investigate the nature of those acoustic features.
QUALITATIVE ANALYSIS OF PLP IN LSTM FOR BANGLA SPEECH RECOGNITIONijma
This document summarizes a study that compares different acoustic feature extraction methods (LPC, MFCC, PLP) for a Bangla speech recognition system using LSTM neural networks. It finds that PLP outperforms MFCC and LPC based on statistical distance measurements of phoneme coefficients. PLP shows better distinction between phonemes compared to MFCC and LPC. While RNN/LSTM are inherently slow, combining PLP with faster networks like Transformers may improve performance for large datasets.
Facial emotion recognition using deep learning detector and classifier IJECEIAES
Numerous research works have been put forward over the years to advance the field of facial expression recognition which until today, is still considered a challenging task. The selection of image color space and the use of facial alignment as preprocessing steps may collectively pose a significant impact on the accuracy and computational cost of facial emotion recognition, which is crucial to optimize the speed-accuracy trade-off. This paper proposed a deep learning-based facial emotion recognition pipeline that can be used to predict the emotion of detected face regions in video sequences. Five well-known state-of-the-art convolutional neural network architectures are used for training the emotion classifier to identify the network architecture which gives the best speed-accuracy trade-off. Two distinct facial emotion training datasets are prepared to investigate the effect of image color space and facial alignment on the performance of facial emotion recognition. Experimental results show that training a facial expression recognition model with grayscale-aligned facial images is preferable as it offers better recognition rates with lower detection latency. The lightweight MobileNet_v1 is identified as the best-performing model with WM=0.75 and RM=160 as its hyperparameters, achieving an overall accuracy of 86.42% on the testing video dataset.
IRJET - Factors Affecting Deployment of Deep Learning based Face Recognition ...IRJET Journal
This document discusses factors affecting the deployment of deep learning models for face recognition on smartphones. It examines training data requirements, suitable neural network architectures, and effective loss functions. Larger datasets with more subjects and images are preferred for training models that generalize well. Residual networks like ResNet have achieved good accuracy while being efficient for face recognition. Loss functions like center loss and triplet loss help learn discriminative features by reducing intra-class and increasing inter-class variations.
This document summarizes a research paper on image-based static facial expression recognition using multiple deep convolutional neural networks. The researchers used an ensemble of face detectors to locate faces in images, then classified the facial expressions using an ensemble of CNN models pre-trained on a larger dataset and fine-tuned on the SFEW 2.0 dataset. They proposed two methods for learning the ensemble weights of the CNN models by minimizing log likelihood or hinge loss. Their method achieved state-of-the-art results on the FER dataset and 61.29% accuracy on the SFEW 2.0 test set, significantly above the baseline.
Possibility fuzzy c means clustering for expression invariant face recognitionIJCI JOURNAL
Face being the most natural method of identification for humans is one of the most significant biometric
modalities and various methods to achieve efficient face recognition have been proposed. However the
changes in face owing to different expressions, pose, makeup, illumination, age bring about marked
variations in the facial image. These changes will inevitably occur and they can be controlled only till a
certain degree beyond which they are bound to happen and will affect the face thereby adversely impacting
the performance of any face recognition system. This paper proposes a strategy to improve the
classification methodology in face recognition by using Possibility Fuzzy C-Means Clustering (PFCM).
This clustering technique was used for face recognition due to its properties like outlier insensitivity which
make it a suitable candidate for use in designing such robust applications.PFCM is a hybridization of
Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) clustering algorithms. PFCM is a robust
clustering technique and is especially significant for its noise insensitivity. It has also resolved the
coincident clusters problem which is faced by other clustering techniques. Therefore the technique can also
be used to increase the overall robustness of a face recognition system and thereby increase its invariance
and make it a reliably usable biometric modality.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...kevig
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
BIDIRECTIONAL LONG SHORT-TERM MEMORY (BILSTM)WITH CONDITIONAL RANDOM FIELDS (...ijnlc
This study investigates the effectiveness of Knowledge Named Entity Recognition in Online Judges (OJs). OJs are lacking in the classification of topics and limited to the IDs only. Therefore a lot of time is consumed in finding programming problems more specifically in knowledge entities.A Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) model is applied for the recognition of knowledge named entities existing in the solution reports.For the test run, more than 2000 solution reports are crawled from the Online Judges and processed for the model output. The stability of the model is
also assessed with the higher F1 value. The results obtained through the proposed BiLSTM-CRF model are more effectual (F1: 98.96%) and efficient in lead-time.
FaceDetectionforColorImageBasedonMATLAB.pdfAnita Pal
The document proposes a method for face detection in color images using MATLAB and a convolutional neural network (CNN) with two layers. It begins with an introduction to face detection technologies and challenges. It then describes the proposed methodology, which involves reading a color image, applying CNN convolutions to detect faces, and fusing the results. Mathematical aspects of CNNs are discussed, including equations for convolutional operations and dimensions. The CNN method combines feature extraction, segmentation, and classification. Finally, the document summarizes how the proposed algorithm was designed in MATLAB to identify faces using a simple learning algorithm and CNNs with fewer layers than traditional networks. The goal is to develop a fast face detection program for color images based on new deep learning standards.
Comparative Study of Different Techniques in Speaker Recognition: ReviewIJAEMSJORNAL
The speech is most basic and essential method of communication used by person.On the basis of individual information included in speech signals the speaker is recognized. Speaker recognition (SR) is useful to identify the person who is speaking. In recent years speaker recognition is used for security system. In this paper we have discussed the feature extraction techniques like Mel frequency cepstral coefficient (MFCC), Linear predictive coding (LPC), Dynamic time wrapping (DTW), and for classification Gaussian Mixture Models (GMM), Artificial neural network (ANN)& Support vector machine (SVM).
We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models.
The document proposes a method for face recognition using deep learning and data augmentation. It cleans and pre-processes existing face datasets to remove noise and extracts faces. It then uses image processing techniques to add masks to the faces to create a new masked face dataset. An Inception Resnet-v1 model is trained on the new dataset. The method is applied to build a face recognition application for employee timekeeping that achieves high accuracy even when faces are masked.
Offline Character Recognition Using Monte Carlo Method and Neural Networkijaia
Human Machine interface are constantly gaining improvements because of increasing development of
computer tools. Handwritten Character Recognition do have various significant applications like form
scanning, verification, validation, or checks reading. Because of the importance of these applications
passionate research in the field of Off-Line handwritten character recognition is going on. The challenge in
recognising the handwritings lies in the nature of humans, having unique styles in terms of font, contours,
etc. This paper presents a novice approach to identify the offline characters; we call it as character divider
approach which can be used after pre-processing stage. We devise an innovative approach for feature
extraction known as vector contour. We also discuss the pros and cons including limitations, of our
approach
Module 04 Content· As a continuation to examining your policies, rIlonaThornburg83
Module 04 Content
· As a continuation to examining your policies, review for procedures that may relate to them.
· In a 4-page paper, describe the procedures for each of the two compliance plans. NOTE: procedures are not the same thing as policies. Policies (Module 03) sets the parameters for decision-making, while procedures explains the "how". The procedures should be step-by-step instructions, and may include a checklist or process steps to follow.
· Break each procedure section into 2 pages each.
· Remember to support your procedures for each of two plans with a total of three research sources (1-2 per procedure), cited at the end in APA format.
· Write your procedures in a way that all employees will understand at a large medical facility where you are the Compliance Officer.
· Remember, you chose two compliance policy plans under the key compliance areas of Compliance Standards, High-Level Responsibility, Education, Communication, Monitoring/Auditing (for Safety), Enforcement/Discipline, and Response/Prevention. (Check them out if you forget! Remember, you may have written about different policies for the two different compliance plans.)
·
· Submit your completed assignment by following the directions linked below. Please check the Course Calendar for specific due dates.
·
· Save your assignment as a Microsoft Word document. (Mac users, please remember to append the ".docx" extension to the filename.) The name of the file should be your first initial and last name, followed by an underscore and the name of the assignment, and an underscore and the date. An example is shown below:
· Jstudent_exampleproblem_101504
1
8
Southeast Missouri State University
Department of Computer Science
Name of the Instructor: Dr. Reshmi Mitra
CS – 609 Graduate Project
December 2021
Team Member List
Jing Ma
Manoj Thapa
Nagendra Mokara
Contents
Machine learning on Stock Predition 0
Abstract 4
1 INTRODUCTION 4
1.1 LSTM Prediction 5
2 BACKGROUND AND RELATED WORK 6
2.1 Overview of the design 7
3 RESEARCH DESIGN AND METHODS 8
3.1 Stock Price Prediction Project using LSTM 8
3.2 Build the dashboard using plotly 9
4 EVALUTION, VALIDATION ANF KEYRESULTS 9
4.1 Screenshot of Result 10
5 CONCLUSIONS 11
References 12
Sound Classification using Deep Learning ……………………………………………...13
Abstract ………………………………………………………………………………….14
6 INTRODUCTION …………………………………………………………………….14
6.1 Keywords ……………………………………………………………………………15
7 COLLECTION OF DATA FROM THE DATASET …………………………………..15
7.1 Dataset ……………………………………………………………………………….15
7.2 Segregation of Data into Various Folders ..………………………………………….15
8 WRITING CODE AND DATA MODELING/TRAINING……………………………16
8.1 Design Diagram of the Project……………………………………………………….16
9 RESULT ……………………………………………………………………………….17
10 CONCLUSION/FUTURE WORK …………………………………………………..18
Machine learning on stock prediction
Jing Ma
December 2021
Southeast Missouri State University
Advisor: Dr Robert Lowe
Course: Researc ...
DeepFace: Closing the Gap to Human-Level Performance in Face VerificationJoão Gabriel Lima
In modern face recognition, the conventional pipeline consists of four stages: detect ⇒ align ⇒ represent ⇒ clas- sify. We revisit both the alignment step and the representa- tion step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight shar- ing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an iden- tity labeled dataset of four million facial images belong- ing to more than 4,000 identities. The learned representa- tions coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approach- ing human-level performance
The face recognition technique gave in this study utilizes a reconfigurable organization of paramount threshold logic cells and can be utilized in the optional layer of a pixel exhibit. The technology used today for face recognition is neither either new nor particularly ancient. Face recognition is primarily employed for security reasons. Real-time applications have seen rapid growth in the demanding and fascinating field of face recognition. In recent years, face recognition has been the subject of intensive research. This report presents an up-to-date review of key human facial recognition studies. We begin by providing a general introduction of face recognition and its uses. The most recent facial recognition methods are then reviewed in the literature.
Similar to Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks (20)
Investing in AI transformation today
The modern business advantage: Uncovering deep insights with AI
Organizations around the world have come to recognize AI as the transformative technology that enables them to gain real business advantage.
AI’s ability to organize vast quantities of data allows those who implement it to uncover deep business insights, augment human expertise, drive
operational efficiency, transform their products, and better serve their customers
Last year’s Global Risks Report warned of a world
that would not easily rebound from continued
shocks. As 2024 begins, the 19th edition of
the report is set against a backdrop of rapidly
accelerating technological change and economic
uncertainty, as the world is plagued by a duo of
dangerous crises: climate and conflict.
Underlying geopolitical tensions combined with the
eruption of active hostilities in multiple regions is
contributing to an unstable global order characterized
by polarizing narratives, eroding trust and insecurity.
At the same time, countries are grappling with the
impacts of record-breaking extreme weather, as
climate-change adaptation efforts and resources
fall short of the type, scale and intensity of climaterelated events already taking place. Cost-of-living
pressures continue to bite, amidst persistently
elevated inflation and interest rates and continued
economic uncertainty in much of the world.
Despondent headlines are borderless, shared
regularly and widely, and a sense of frustration at
the status quo is increasingly palpable. Together,
this leaves ample room for accelerating risks – like
misinformation and disinformation – to propagate
in societies that have already been politically and
economically weakened in recent years.
Just as natural ecosystems can be pushed to the
limit and become something fundamentally new;
such systemic shifts are also taking place across
other spheres: geostrategic, demographic and
technological. This year, we explore the rise of global
risks against the backdrop of these “structural
forces” as well as the tectonic clashes between
them. The next set of global conditions may not
necessarily be better or worse than the last, but the
transition will not be an easy one.
The report explores the global risk landscape in this
phase of transition and governance systems being
stretched beyond their limit. It analyses the most
severe perceived risks to economies and societies
over two and 10 years, in the context of these
influential forces. Could we catapult to a 3°C world
as the impacts of climate change intrinsically rewrite
the planet? Have we reached the peak of human
development for large parts of the global population,
given deteriorating debt and geo-economic
conditions? Could we face an explosion of criminality
and corruption that feeds on more fragile states and
more vulnerable populations? Will an “arms race” in
experimental technologies present existential threats
to humanity?
These transnational risks will become harder to
handle as global cooperation erodes. In this year’s
Global Risks Perception Survey, two-thirds of
respondents predict that a multipolar order will
dominate in the next 10 years, as middle and
great powers set and enforce – but also contest
- current rules and norms. The report considers
the implications of this fragmented world, where
preparedness for global risks is ever more critical but
is hindered by lack o
KOSMOS-1 is a multimodal large language model that can perceive and process language as well as visual inputs like images. It was trained on large web-scale datasets containing text, images, and image-caption pairs to align its vision capabilities with its natural language understanding. Experimental results showed that KOSMOS-1 can perform well on tasks involving language, vision, and their combination, including image captioning, visual question answering, and describing images based on text instructions, all without any fine-tuning. The ability to perceive and understand different modalities allows language models to acquire knowledge in new ways and expands their application to areas like robotics and document intelligence.
We present a causal speech enhancement model working on the
raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with
skip-connections. It is optimized on both time and frequency
domains, using multiple loss functions. Empirical evidence
shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises,
as well as room reverb. Additionally, we suggest a set of
data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. We perform evaluations on several standard
benchmarks, both using objective metrics and human judgements. The proposed model matches state-of-the-art performance of both causal and non causal methods while working
directly on the raw waveform.
Index Terms: Speech enhancement, speech denoising, neural
networks, raw waveform
This document discusses IBM's reference architecture for data and AI. It provides guidance on designing systems that use AI and analyze large amounts of data. The reference architecture covers strategies for collecting, storing, processing and analyzing data at large scales using technologies like Apache Spark, Hadoop and containers. It is intended to help organizations build systems that extract insights from data.
1. El documento analiza las oportunidades y desafíos que plantea la inteligencia artificial para el crecimiento económico de Colombia. 2. Señala que la IA podría acelerar el crecimiento de Colombia en aproximadamente 1 punto porcentual anual durante la próxima década si se logra aumentar la tasa de adopción tecnológica. 3. Sin embargo, también destaca que esto requiere que las empresas colombianas sean más dinámicas en la absorción de nuevas tecnologías y que la fuerza laboral desarrolle
Artificial neural networks are the heart of machine learning algorithms and artificial intelligence
protocols. Historically, the simplest implementation of an artificial neuron traces back to the classical
Rosenblatt’s “perceptron”, but its long term practical applications may be hindered by the fast scal-
ing up of computational complexity, especially relevant for the training of multilayered perceptron
networks. Here we introduce a quantum information-based algorithm implementing the quantum
computer version of a perceptron, which shows exponential advantage in encoding resources over
alternative realizations. We experimentally test a few qubits version of this model on an actual
small-scale quantum processor, which gives remarkably good answers against the expected results.
We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier
of simple patterns, as a first step towards practical training of artificial quantum neural networks
to be efficiently implemented on near-term quantum processing hardware
En los ̇ltimos 20 aÒos la Enfermedad de Alzheimer pasÛ de ser el paradigma
del envejecimiento normal -aunque prematuro y acelerado-, del cerebro,
para convertirse en una enfermedad autÈntica, nosolÛgicamente bien defini-
da y con una clara raÌz genÈtica. La enfermedad afecta hoy a m·s de 20
millones de personas, tiene enormes consecuencias sobre la economÌa de los
paÌses y constituye uno de los temas de investigaciÛn m·s activos en el ·rea
de salud.
Este artÌculo revisa el conocimiento actual sobre el tema. En esta primera
parte se analizan su epidemiologÌa, patogenia y genÈtica; se enumeran los
temas prioritarios de investigaciÛn; se revisa su relaciÛn con el concepto de
muerte celular programada (apoptosis) y se enumeran los elementos indis-
pensables para el diagnÛstico.
Palabras Clave
:Enfermedad de Alzhaimer; Demencia; GenÈtica; TerapÈuti-
ca.
Artificial intelligence and machine learning capabilities are growing at an unprecedented rate. These technologies have many widely beneficial applications, ranging from machine translation to medical image analysis. Countless more such applications are being developed and can be expected over the long term. Less attention has historically been paid to the ways in which artificial intelligence can be used maliciously. This report surveys the landscape of potential security threats from malicious uses of artificial intelligence technologies, and proposes ways to better forecast, prevent, and mitigate these threats. We analyze, but do not conclusively resolve, the question of what the long-term equilibrium between attackers and defenders will be. We focus instead on what sorts of attacks we are likely to see soon if adequate defenses are not developed.
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals’ mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users’ level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metricspresentedintheliteratureandemploythesemetricsasinputtothenetwork. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.
La Hipertensión, es una de las mayores enfermedades que sufren los Hispanohablantes en el planeta . Es grato poder colocar este documento al público y haber podido hacer parte del equipo , ojalá sirvan a muchos las implementaciones. idioma más hablado según el foro Económico mundial - Me refiero al español ó castellano según sea -
segundo idioma y haber podido hacer parte de este equipo. Genuinamente, espero que se curen la mayor cantidad de personas con . Espero genuinamente puedan hacer algúna donación a este esfuerzo grupal. Espero Compartamos este "Paper" así como compartimos memes - En el sentido literal de la significancia-
** Refierase a Wikipedia sino tiene un diccionario a mano.
To thrive in the 21st century, students need more than traditional academic learning. They must be adept at collaboration, communication and problem-solving, which are some of the skills developed through social and emotional learning (SEL). Coupled with mastery of traditional skills, social and emotional proficiency will equip students to succeed in the swiftly evolving digital economy. In 2015, the World Economic Forum published a report that focused on the pressing issue of the 21st-century skills gap and ways to address it through technology (New Vision for Education: Unlocking the Potential of Technology). In that report, we defined a set of 16 crucial proficiencies for education in the 21st century. Those skills include six “foundational literacies”, such as literacy, numeracy and scientific literacy, and 10 skills that we labelled either “competencies” or “character qualities”. Competencies are the means by which students approach complex challenges; they include collaboration, communication and critical thinking and problem-solving. Character qualities are the ways in which students approach their changing environment; they include curiosity, adaptability and social and cultural awareness (see Exhibit 1).
In our current report, New Vision for Education: Fostering Social and Emotional Learning through Technology, we follow up on our 2015 report by exploring how these competencies and character qualities do more than simply deepen 21st-century skills. Together, they lie at the heart of SEL and are every bit as important as the foundational skills required for traditional academic learning. Although many stakeholders have defined SEL more narrowly, we believe the definition of SEL is evolving. We define SEL broadly to encompass the 10 competencies and character qualities.1 As is the case with traditional academic learning, technology can be invaluable at enabling SEL.
La expresión “futuro del trabajo” es actualmente uno de los conceptos más populares en las búsquedas en Google. Los numerosos avances tecnológicos de los últimos tiempos están modificando rápidamente la frontera entre las actividades realizadas por los seres humanos y las ejecutadas por las máquinas, lo cual está transformando el mundo del trabajo. Existe un creciente número de estudios e iniciativas que se están llevando a cabo con el objeto de analizar qué significan estos cambios en nuestro trabajo, en nuestros ingresos, en el futuro de nuestros hijos, en nuestras empresas y en nuestros gobiernos. Estos análisis se conducen principalmente desde la óptica de las economías avanzadas, y mucho menos desde la perspectiva de las economías en desarrollo y emergentes. Sin embargo, las diferencias en materia de difusión tecnológica, de estructuras económicas y demográficas, de niveles de educación y patrones
migratorios inciden de manera significativa en la manera en que estos cambios pueden afectar a los países en desarrollo y emergentes. Este estudio, El futuro del trabajo: perspectivas regionales, se centra en las repercusiones probables de estas tendencias en las economías en desarrollo y emergentes de África; Asia; Europa del Este, Asia Central y el Mediterráneo Sur y Oriental, y América Latina y el Caribe. Se trata de un esfuerzo mancomunado de los cuatro principales bancos regionales de desarrollo: el African Development Bank Group, el Asian Development Bank, el Banco Interamericano de Desarrollo y el European Bank for Reconstruction and Development. En el estudio se destacan las oportunidades que los cambios en la dinámica del trabajo podrían crear en nuestras regiones. El progreso tecnológico permitiría a los países con los que trabajamos crecer y alcanzar rápidamente mejores niveles de vida que en el pasado
El documento resume el ascenso de China como potencia mundial y cómo esto está cuestionando el orden internacional liderado por Estados Unidos. Explica que tras la Guerra Fría hubo un período de optimismo sobre la expansión de la democracia y la globalización, pero que ahora Estados Unidos se está retirando de su liderazgo global mientras China se fortalece económica y militarmente. Predice que Asia será el epicentro de los cambios geopolíticos a medida que China busque aumentar su influencia en la región.
The increasing use of electronic forms of communication presents
new opportunities in the study of mental health, including the
ability to investigate the manifestations of psychiatric diseases un-
obtrusively and in the setting of patients’ daily lives. A pilot study to
explore the possible connections between bipolar affective disorder
and mobile phone usage was conducted. In this study, participants
were provided a mobile phone to use as their primary phone. This
phone was loaded with a custom keyboard that collected metadata
consisting of keypress entry time and accelerometer movement.
Individual character data with the exceptions of the backspace key
and space bar were not collected due to privacy concerns. We pro-
pose an end-to-end deep architecture based on late fusion, named
DeepMood, to model the multi-view metadata for the prediction
of mood scores. Experimental results show that 90.31% prediction
accuracy on the depression score can be achieved based on session-
level mobile phone typing dynamics which is typically less than
one minute. It demonstrates the feasibility of using mobile phone
metadata to infer mood disturbance and severity
Defin
ing artificial intelligence is no easy matter. Since the mid
-
20th century when it
was first
recognized
as a specific field of research, AI has always been envisioned as
an evolving boundary, rather than a settled research field. Fundamentally, it refers
to
a programme whose ambitious objective is to understand and reproduce human
cognition; creating cognitive processes comparable to those found in human beings.
Therefore, we are naturally dealing with a wide scope here, both in terms of the
technical proced
ures that can be employed and the various disciplines that can be
called upon: mathematics, information technology, cognitive sciences, etc. There is
a great variety of approaches when it comes to AI: ontological, reinforcement
learning, adversarial learni
ng and neural networks, to name just a few. Most of them
have been known for decades and many of the algorithms used today were
developed in the ’60s and ’70s.
Since the 1956 Dartmouth conference, artificial intelligence has alternated between
periods of
great enthusiasm and disillusionment, impressive progress and frustrating
failures. Yet, it has relentlessly pushed back the limits of what was only thought to
be achievable by human beings. Along the way, AI research has achieved significant
successes: o
utperforming human beings in complex games (chess, Go),
understanding natural language, etc. It has also played a critical role in the history
of mathematics and information technology. Consider how many softwares that we
now take for granted once represen
ted a major breakthrough in AI: chess game
apps, online translation programmes, etc
Researchers surveyed 352 AI experts about their predictions for advances in artificial intelligence. The key findings were:
- Researchers predicted that AI will outperform humans in many tasks within the next 10 years, such as translating languages by 2024 and driving a truck by 2027.
- The experts estimated a 50% chance of AI outperforming humans in all tasks within 45 years and automating all human jobs within 120 years. Asian respondents expected these dates to occur much sooner than North American respondents.
- The researchers believed that progress in machine learning has accelerated in recent years. They saw the possibility of an "intelligence explosion" after high-level machine intelligence is achieved but estimated the probability as low, around 10
The document provides an overview of Microsoft's AI platform, which includes AI Services, Infrastructure, and Tools. The platform offers a comprehensive set of AI services for rapid development, enterprise-grade infrastructure to run AI workloads at scale, and modern tools for data scientists and developers to create and operationalize AI solutions. It allows building intelligent applications that augment human abilities across various industries.
This document proposes AttnGAN, an Attentional Generative Adversarial Network for fine-grained text-to-image generation. AttnGAN uses an attentional generative network with multiple generators that produce higher resolution images at each stage. It attends to relevant words for different image regions using attention models. AttnGAN also uses a Deep Attentional Multimodal Similarity Model to compute an image-text matching loss for training. Experimental results show AttnGAN significantly outperforms previous methods on benchmark datasets.
The Hamilton Project • Brookings i
Seven Facts on Noncognitive Skills
from Education to the Labor Market
Introduction
Cognitive skills—that is, math and reading skills that are measured by standardized tests—are generally
understood to be of critical importance in the labor market. Most people find it intuitive and indeed
unsurprising that cognitive skills, as measured by standardized tests, are important for students’ later-life
outcomes. For example, earnings tend to be higher for those with higher levels of cognitive skills. What is
less well understood—and is the focus of these economic facts—is that noncognitive skills are also integral to
educational performance and labor-market outcomes.
Due in large part to research pioneered in economics by Nobel laureate James J. Heckman, there is a robust and
growing body of evidence that noncognitive skills function similarly to cognitive skills, strongly improving
labor-market outcomes. These noncognitive skills—often referred to in the economics literature as soft skills and
elsewhere as social, emotional, and behavioral skills—include qualities like perseverance, conscientiousness,
and self-control, as well as social skills and leadership ability (Duckworth and Yeager 2015). The value of these
qualities in the labor market has increased over time as the mix of jobs has shifted toward positions requiring
noncognitive skills. Evidence suggests that the labor-market payoffs to noncognitive skills have been increasing
over time and the payoffs are particularly strong for individuals who possess both cognitive and noncognitive
skills (Deming 2015; Weinberger 2014).
Although we draw a conceptual distinction between noncognitive skills and cognitive skills, it is not possible to
disentangle these concepts fully. All noncognitive skills involve cognition, and some portion of performance on
cognitive tasks is made possible by noncognitive skills. For the purposes of this document, the term “cognitive
skills” encompasses intelligence; the ability to process, learn, think, and reason; and substantive knowledge
as reflected in indicators of academic achievement. Since the No Child Left Behind Act of 2001, education
policy has focused on accountability policies aimed at improving cognitive skills and closing test score gaps
across groups. These policies have been largely successful, particularly for math achievement (Dee and Jacob
2011; Wong, Cook, and Steiner 2009) and among students most exposed to accountability pressure (Neal and
Schanzenbach 2010). What has received less attention in policy debates is the importance of noncognitive skills.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
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https://www.meetup.com/unstructured-data-meetup-new-york/
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The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
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Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
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#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
2. Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural
Networks
Behzad Hasani and Mohammad H. Mahoor
Department of Electrical and Computer Engineering
University of Denver, Denver, CO
behzad.hasani@du.edu and mmahoor@du.edu
Abstract
Deep Neural Networks (DNNs) have shown to outper-
form traditional methods in various visual recognition tasks
including Facial Expression Recognition (FER). In spite of
efforts made to improve the accuracy of FER systems using
DNN, existing methods still are not generalizable enough
in practical applications. This paper proposes a 3D Con-
volutional Neural Network method for FER in videos. This
new network architecture consists of 3D Inception-ResNet
layers followed by an LSTM unit that together extracts the
spatial relations within facial images as well as the tem-
poral relations between different frames in the video. Fa-
cial landmark points are also used as inputs to our network
which emphasize on the importance of facial components
rather than the facial regions that may not contribute sig-
nificantly to generating facial expressions. Our proposed
method is evaluated using four publicly available databases
in subject-independent and cross-database tasks and out-
performs state-of-the-art methods.
1. Introduction
Facial expressions are one of the most important non-
verbal channels for expressing internal emotions and in-
tentions. Ekman et al. [13] defined six expressions (viz.
anger, disgust, fear, happiness, sadness, and surprise) as ba-
sic emotional expressions which are universal among hu-
man beings. Automated Facial Expression Recognition
(FER) has been a topic of study for decades. Although
there have been many breakthroughs in developing auto-
matic FER systems, majority of the existing methods either
show undesirable performance in practical applications or
lack generalization due to the controlled condition in which
they are developed [47].
The FER problem becomes even more difficult when we
recognize expressions in videos. Facial expressions have a
dynamic pattern that can be divided into three phases: on-
Figure 1. Proposed method
set, peak and offset, where the onset describes the beginning
of the expression, the peak (aka apex) describes the maxi-
mum intensity of the expression and the offset describes the
moment when the expression vanishes. Most of the times,
the entire event of facial expression from the onset to the
offset is very quick, which makes the process of expression
recognition very challenging [55].
Many methods have been proposed for automated facial
expression recognition. Most of the traditional approaches
mainly consider still images independently while ignore the
temporal relations of the consecutive frames in a sequence
which are essential for recognizing subtle changes in the
appearance of facial images especially in transiting frames
between emotions. Recently, with the help of Deep Neu-
ral Networks (DNNs), more promising results are reported
in the field [38, 39]. While in traditional approaches engi-
neered features are used to train classifiers, DNNs have the
ability to extract more discriminative features which yield
in a better interpretation of the texture of human face in vi-
sual data.
One of the problems in FER is that training neural net-
1
arXiv:1705.07871v1[cs.CV]22May2017
3. works is significantly more difficult as most of the exist-
ing databases have a small number of images or video se-
quences for certain emotions [39]. Also, most of these
databases contain still images that are unrelated to each
other (instead of having consecutive frames of exhibiting
the expression from onset to offset) which makes the task
of sequential image labeling more difficult.
In this paper, we propose a method which extracts tem-
poral relations of consecutive frames in a video sequence
using 3D convolutional networks and Long Short-Term
Memory (LSTM). Furthermore, we extract and incorporate
facial landmarks in our proposed method that emphasize
on more expressive facial components which improve the
recognition of subtle changes in the facial expressions in
a sequence (Figure 1). We evaluate our proposed method
using four well-known facial expression databases (CK+,
MMI, FERA, and DISFA) in order to classify the expres-
sions. Furthermore, we examine the ability of our method
in recognition of facial expressions in cross-database clas-
sification tasks.
The remainder of the paper is organized as follows: Sec-
tion 2 provides an overview of the related work in this field.
Section 3 explains the network proposed in this research.
Experimental results and their analysis are presented in Sec-
tion 4 and finally the paper is concluded in Section 5.
2. Related work
Traditionally, algorithms for automated facial expression
recognition consist of three main modules, viz. registration,
feature extraction, and classification. Detailed survey of dif-
ferent approaches in each of these steps can be found in
[44]. Conventional algorithms for affective computing from
faces use engineered features such as Local Binary Patterns
(LBP) [47], Histogram of Oriented Gradients (HOG) [8],
Local Phase Quantization (LPQ) [59], Histogram of Optical
Flow [9], facial landmarks [6, 7], and PCA-based methods
[36]. Since the majority of these features are hand-crafted
for their specific application of recognition, they often lack
required generalizability in cases where there is high varia-
tion in lighting, views, resolution, subjects’ ethnicity, etc.
One of the effective approaches for achieving better
recognition rates for sequence labeling task is to extract
the temporal relations of frames in a sequence. Extract-
ing these temporal relations has been studied using tradi-
tional methods in the past. Examples of these attempts are
Hidden Markov Models [5, 60, 64] (which combine tempo-
ral information and apply segmentation on videos), Spatio-
Temporal Hidden Markov Models (ST-HMM) by coupling
S-HMM and T-HMM [50], Dynamic Bayesian Networks
(DBN) [45, 63] associated with a multi-sensory informa-
tion fusion strategy, Bayesian temporal models [46] to cap-
ture the dynamic facial expression transition, and Condi-
tional Random Fields (CRFs) [19, 20, 25, 48] and their
extensions such as Latent-Dynamic Conditional Random
Fields (LD-CRFs) and Hidden Conditional Random Fields
(HCRFs) [58].
In recent years, “Convolutional Neural Networks”
(CNNs) have become the most popular approach among
researchers in the field. AlexNet [27] is based on the tra-
ditional CNN layered architecture which consists of sev-
eral convolution layers followed by max-pooling layers and
Rectified Linear Units (ReLUs). Szegedy et al. [52] intro-
duced GoogLeNet which is composed of multiple “Incep-
tion” layers. Inception applies several convolutions on the
feature map in different scales. Mollahosseini et al. [38, 39]
have used the Inception layer for the task of facial expres-
sion recognition and achieved state-of-the-art results. Fol-
lowing the success of Inception layers, several variations
of them have been proposed [24, 53]. Moreover, Inception
layer is combined with residual unit introduced by He et al.
[21] and it shows that the resulting architecture accelerates
the training of Inception networks significantly [51].
One of the major restrictions of ordinary Convolutional
Neural Networks is that they only extract spatial relations of
the input data while ignore the temporal relations of them if
they are part of a sequenced data. To overcome this prob-
lem, 3D Convolutional Neural Networks (3D-CNNs) have
been proposed. 3D-CNNs slide over the temporal dimen-
sion of the input data as well as the spatial dimension en-
abling the network to extract feature maps containing tem-
poral information which is essential for sequence labeling
tasks. Song et al. [49] have used 3D-CNNs for 3D object
detection task. Molchanov et al. [37] have proposed a re-
current 3D-CNN for dynamic hand gesture recognition and
Fan et al. [15] won the EmotiW 2016 challenge by cascad-
ing 3D-CNNs with LSTMs.
Traditional Recurrent Neural Networks (RNNs) can
learn temporal dynamics by mapping input sequences to
a sequence of hidden states, and also mapping the hidden
states to outputs [12]. Although RNNs have shown promis-
ing performance on various tasks, it is not easy for them
to learn long-term sequences. This is mainly due to the
vanishing/exploding gradients problem [23] which can be
solved by having a memory for remembering and forget-
ting the previous states. LSTMs [23] provide such memory
and can memorize the context information for long peri-
ods of time. LSTM modules have three gates: 1) the input
gate (i) 2) the forget gate (f) and 3) the output gate (o)
which overwrite, keep, or retrieve the memory cell c respec-
tively at the timestep t. Letting σ(x) = (1 + exp(−x))−1
be the sigmoid function and φ(x) = exp(x)−exp(−x)
exp(x)+exp(−x) =
2σ(2x) − 1 be the hyperbolic tangent function. Letting
x, h, c, W, and b be the input, output, cell state, parameter
matrix, and parameter vector respectively. The LSTM up-
dates for the timestep t given inputs xt, ht−1, and ct−1 are
as follows:
4. ft = σ(Wf · [ht−1, xt] + bf )
it = σ(Wi · [ht−1, xt] + bi)
ot = σ(Wo · [ht−1, xt] + bo)
gt = φ(WC · [ht−1, xt] + bC)
Ct = ft ∗ Ct−1 + it ∗ gt
ht = ot ∗ φ(Ct)
(1)
Several works have used LSTMs for the task of sequence
labeling. Byeon et al. [3] proposed an LSTM-based net-
work applying LSTMs in four direction sliding windows
and achieved impressive results. Fan et al. [15] cascaded
2D-CNN with LSTMs and combined the feature map with
3D-CNNs for facial expression recognition. Donahue et
al. [12] proposed Long-term Recurrent Convolutional Net-
work (LRCN) by combining CNNs and LSTMs which is
both spatially and temporally deep and has the flexibility
to be applied to different vision tasks involving sequential
inputs and outputs.
3. Proposed method
While Inception and ResNet have shown remarkable re-
sults in FER [20, 52], these methods do not extract the tem-
poral relations of the input data. Therefore, we propose
a 3D Inception-ResNet architecture to address this issue.
Our proposed method, extracts both spatial and temporal
features of the sequences in an end-to-end neural network.
Another component of our method is incorporating facial
landmarks in an automated manner during training in the
proposed neural network. These facial landmarks help the
network to pay more attention to the important facial com-
ponents in the feature maps which results in a more accurate
recognition. The final part of our proposed method is an
LSTM unit which takes the enhanced feature map resulted
from the 3D Inception-ResNet (3DIR) layer as an input and
extracts the temporal information from it. The LSTM unit is
followed by a fully-connected layer associated with a soft-
max activation function. In the following, we explain each
of the aforementioned units in detail.
3.1. 3D Inception-ResNet (3DIR)
We propose 3D version of Inception-ResNet network
which is slightly shallower than the original Inception-
ResNet network proposed in [51]. This network is the re-
sult of investigating several variations of Inception-ResNet
module and achieves better recognition rates comparing to
our other attempts in several databases.
Figure 2 shows the structure of our 3D Inception-ResNet
network. The input videos with the size 10 ×299×299 ×3
(10 frames, 299 × 299 frame size and 3 color channels) are
followed by the “stem” layer. Afterwards, stem is followed
Figure 2. Network architecture. The “V” and “S” marked layers
represent “Valid” and “Same” paddings respectively. The size of
the output tensor is provided next to each layer.
by 3DIR-A, Reduction-A (which reduces the grid size from
38 × 38 to 18 × 18), 3DIR-B, Reduction-B (which reduces
the grid size from 18×18 to 8×8), 3DIR-C, Average Pool-
ing, Dropout, and a fully-connected layer respectively. In
Figure 2, detailed specification of each layer is provided.
Various filter sizes, paddings, strides, and activations have
been investigated and the one that had the best performance
is presented in this paper.
We should mention that all convolution layers (except
the ones that are indicated as “Linear” in Figure 2) are fol-
5. lowed by an ReLU [27] activation function to avoid the van-
ishing gradient problem.
3.2. Facial landmarks
As mentioned before, the main reason we use facial land-
marks in our network is to differentiate between the impor-
tance of main facial components (such as eyebrows, lip cor-
ners, eyes, etc.) and other parts of the face which are less
expressive of facial expressions. As oppose to general ob-
ject recognition task, in FER, we have the advantage of ex-
tracting facial landmarks and using this information to im-
prove the recognition rate. In a similar approach, Jaiswal
et al. [26] proposed incorporation of binary masks around
different parts of the face in order to encode the shape of
different face components. However, in this work authors
perform AU recognition by using CNN as a feature extrac-
tor for training Bi-directional Long Short-Term Memory
while in our approach, we preserve the temporal order of the
frames throughout the network and train CNN and LSTMs
simultaneously in an end-to-end network. We incorporate
the facial landmarks by replacing the shortcut in residual
unit on original ResNet with element-wise multiplication of
facial landmarks and the input tensor of the residual unit
(Figures 1 and 2).
In order to extract the facial landmarks, OpenCV face
recognition is used to obtain bounding boxes of the faces.
A face alignment algorithm via regression local binary
features [41, 61] was used to extract 66 facial landmark
points. The facial landmark localization technique was
trained using the annotations provided from the 300W com-
petition [42, 43].
After detecting and saving the facial landmarks for all of
the databases, the facial landmark filters are generated for
each sequence automatically during training phase. Given
the facial landmarks for each frame of a sequence, we ini-
tially resize all of the images in the sequence to their cor-
responding filter size in the network. Afterwards, we as-
sign weights to all of the pixels in a frame of a sequence
based on their distances to the detected landmarks. The
closer a pixel is to a facial landmark, the greater weight is
assigned to that pixel. After investigating several distance
measures, we concluded that Manhattan distance with a
linear weight function results in a better recognition rate
in various databases. The Manhattan distance between two
items is the sum of the differences of their corresponding
components (in this case two components).
The weight function that we defined to assign the weight
values to their corresponding feature is a simple linear func-
tion of the Manhattan distance defined as follows:
ω(L, P) = 1 − 0.1 · dM(L,P ) (2)
where dM(L,P ) is the Manhattan distance between the facial
landmark L and pixel P. Therefore, places in which facial
(a) Landmarks (b) Generated filter
Figure 3. Sample image from MMI database (left) and its corre-
sponding filter in the network (right). Best in color.
landmarks are located will have the highest value and their
surrounding pixels will have lower weights proportional
to their distance from the corresponding facial landmark.
In order to avoid overlapping between two adjacent facial
landmarks, we define a 7 × 7 window around each facial
landmark and apply the weight function for these 49 pixels
for each landmark separately. Figure 3 shows an example
of facial image from MMI database and its corresponding
facial landmark filter in the network. We do not incorporate
the facial landmarks with the third 3D Inception-ResNet
module since the resulting feature map size at this stage be-
comes very small for calculating facial landmark filter.
Incorporating facial landmarks in our network replaces
the shortcut in original ResNets [22] with the element-wise
multiplication of the weight function ω and input layer xl
as follows:
yl = ω(L, P) ◦ xl + F(xl, Wl)
xl+1 = f(yl)
(3)
where xl and xl+1 are input and output of the l-th layer, ◦ is
Hadamard product symbol, F is a residual function (in our
case Inception layer convolutions), and f is an activation
function.
3.3. Long Short-Term Memory unit
As explained earlier, to capture the temporal relations
of the resulted feature map from 3DIR and take these rela-
tions into account by the time of classifying the sequences
in the softmax layer, we used an LSTM unit as it is shown in
Figure 2. Using the LSTM unit makes perfect sense since
the resulted feature map from the 3DIR unit contains the
time notion of the sequences within the feature map. There-
fore, vectorizing the resulting feature map of 3DIR on its se-
quence dimension, will provide the required sequenced in-
put for the LSTM unit. While other still image LSTM-based
methods, a vectorized non-sequenced feature map (which
obviously does not contain any time notion) is fed to the
LSTM unit, our method saves the time order of the input
sequences and passes this feature map to the LSTM unit.
We investigated that 200 hidden units for the LSTM unit is
a reasonable amount for the task of FER (Figure 2).
6. The proposed network was implemented using a com-
bination of TensorFlow [1] and TFlearn [10] toolboxes on
NVIDIA Tesla K40 GPUs. In the training phase we used
asynchronous stochastic gradient descent with momentum
of 0.9, weight decay of 0.0001, and learning rate of 0.01.
We used categorical cross entropy as our loss function and
accuracy as our evaluation metric.
4. Experiments and results
In this section, we briefly review the databases we used
for evaluating our method. We then report the results of our
experiments using these databases and compare the results
with the state of the arts.
4.1. Face databases
Since our method is designed mainly for classifying se-
quences of inputs, databases that contain only independent
unrelated still images of facial expressions such as Mul-
tiPie [18] , SFEW [11] , FER2013 [17] cannot be exam-
ined by our method. We evaluate our proposed method on
MMI [40], extended CK+ [32], GEMEP-FERA [2], and
DISFA [33] which contain videos of annotated facial ex-
pressions. In the following, we briefly review the contents
of these databases.
MMI: The MMI [40] database contains more than 20
subjects, ranging in age from 19 to 62, with different eth-
nicities (European, Asian, or South American). In MMI,
the subjects’ facial expressions start from the neutral state to
the apex of one of the six basic facial expressions and then
returns to the neutral state again. Subjects were instructed
to display 79 series of facial expressions, six of which are
prototypic emotions (angry, disgust, fear, happy, sad, and
surprise). We extracted static frames from each sequence,
which resulted in 11,500 images. Afterwards, we divided
videos into sequences of ten frames to shape the input ten-
sor for our network.
CK+: The extended Cohn-Kanade database (CK+) [32]
contains 593 videos from 123 subjects. However, only 327
sequences from 118 subjects contain facial expression la-
bels. Sequences in this database start from the neutral state
and end at the apex of one of the six basic expressions (an-
gry, contempt, disgust, fear, happy, sad, and surprise). CK+
primarily contains frontal face poses only. In order to make
the database compatible with our network, we consider the
last ten frames of each sequence as an input sequence in our
network.
FERA: The GEMEP-FERA database [2] is a subset of
the GEMEP corpus used as database for the FERA 2011
challenge [56] developed by the Geneva Emotion Research
Group at the University of Geneva. This database contains
87 image sequences of 7 subjects. Each subject shows fa-
cial expressions of the emotion categories: Anger, Fear, Joy,
Relief, and Sadness. Head pose is primarily frontal with rel-
atively fast movements. Each video is annotated with AUs
and holistic expressions. By extracting static frames from
the sequences, we obtained around 7,000 images. We di-
vided the these emotion videos into sequences of ten frames
to shape the input tensor for our network.
DISFA: Denver Intensity of Spontaneous Facial Actions
(DISFA) database [33] is one of a few naturalistic databases
that have been FACS coded by AU intensity values. This
database consists of 27 subjects. The subjects are asked to
watch YouTube videos while their spontaneous facial ex-
pressions are recorded. Twelve AUs are coded for each
frame and AU intensities are on a six-point scale between 0-
5, where 0 denotes the absence of the AU, and 5 represents
maximum intensity. As DISFA is not emotion-specified
coded, we used EMFACS system [16] to convert AU FACS
codes to seven expressions (angry, disgust, fear, happy, neu-
tral, sad, and surprise) which resulted in around 89,000 im-
ages in which the majority have neutral expressions. Same
as other databases, we divided the videos of emotions into
sequences of ten frames to shape the input tensor for our
network.
4.2. Results
As mentioned earlier, after detecting faces we extract 66
facial landmark points by a face alignment algorithm via
regression local binary features. Afterwards, we resize the
faces to 299×299 pixels. One of the reasons why we choose
large image size as input is the fact that larger images and
sequences will enable us to have deeper networks and ex-
tract more abstract features from sequences. All of the net-
works have the same settings (shown in Figure 2 in detail)
and are trained from scratch for each database separately.
We evaluate the accuracy of our proposed method with
two different sets of experiments: “subject-independent”
and “cross-database” evaluations.
4.2.1 Subject-independent task
In the subject-independent task, each database is split into
training and validation sets in a strict subject independent
manner. In all databases, we report the results using the
5-fold cross-validation technique and then averaging the
recognition rates over five folds. For each database and
each fold, we trained our proposed network entirely from
scratch with the aforementioned settings. Table 1 shows the
recognition rates achieved on each database in the subject-
independent case and compares the results with the state-of-
the-art methods. In order to compare the impact of incorpo-
rating facial landmarks, we also provide the results of our
network while the landmark multiplication unit is removed
and replaced with a simple shortcut between the input and
output of the residual unit. In this case, we randomly se-
7. lect 20 percent of the subjects as the test set and report the
results on those subjects. Table 1 also provides the recogni-
tion rates of the traditional 2D Inception-ResNet from [20]
which does not contain facial landmarks and the LSTM unit
(DISFA is not experimented in this study).
Comparing the recognition rates of the 3D and 2D
Inception-ResNets in Table 1, shows that the sequential
processing of facial expressions considerably enhances the
recognition rate. This improvement is more apparent in
MMI and FERA databases. Incorporating landmarks in the
network is proposed to emphasize on more important fa-
cial changes over time. Since changes in the lips or eyes
are much more expressive than the changes in other com-
ponents such as the cheeks, we utilize facial landmarks to
enhance these temporal changes in the network flow.
The “3D Inception-ResNet with landmarks” column in
Table 1 shows the impact of this enhancement in different
databases. It can be seen that compared with other net-
works, there is a considerable improvement in recognition
rates especially in FERA and MMI databases. The results
on DISFA, however, show higher fluctuations over different
folds which can be in part due to the abundance of inactive
frames in this database which causes confusion in recogniz-
ing different expressions. Therefore, the folds that contain
more neutral faces, would show lower recognition rates.
Comparing to other state-of-the-art works, our method
outperforms others in FERA and DISFA databases while
achieves comparable results in CK+ and MMI databases
(Table 1). Most of these works use traditional approaches
including hand-crafted features tuned for that specific
database, while our network’s settings are the same for all
databases. Also, due to the limited number of samples in
these databases, it is difficult to properly train a deep neural
network and avoid the overfitting problem. For these rea-
sons and in order to have a better understanding about our
proposed method, we also experimented the cross-database
task.
Figure 4 shows the resulting confusion matrices of our
3D Inception-ResNet with incorporating landmarks on dif-
ferent databases over the 5 folds. On CK+ (Figure 4a), it can
be seen that very high recognition rates have been achieved.
The recognition rates of happiness, sadness, and surprise are
higher than those of other expressions. The highest confu-
sion occurred between the happiness and contempt expres-
sions which can be caused from the low number of contempt
sequences in this database (only 18 sequences). On MMI
(Figure 4b), a perfect recognition is achieved for the happy
expression. It can be seen that there is a high confusion
between the sad and fear expressions as well as the angry
and sad expressions. Considering the fact that MMI is a
highly imbalanced dataset, these confusions are reasonable.
On FERA (Figure 4c), the highest and the lowest recogni-
tion rates belong to joy and relief respectively. The relief
category in this database has some similarities with other
categories especially with joy. These similarities make the
classification so difficult even for humans. Despite these
challenges, our method has performed well on all of the cat-
egories and outperforms state of the arts. On DISFA (Fig-
ure 4d), we can see the highest confusion rate compared
with other databases. As mentioned earlier, this database
contains long inactive frames, which means that the number
of neutral sequences is considerably higher than other cate-
gories. This imbalanced training data has made the network
to be biased toward the neutral category and therefore we
can observe a high confusion rate between the neutral ex-
pression and other categories in this database. Despite the
low number of angry and sad sequences in this database, our
method has been able achieve satisfying recognition rates in
these categories.
4.2.2 Cross-database task
In the cross-database task, for testing each database, that
database is entirely used for testing the network and the rest
of the databases are used to train the network. The same
network architecture as subject-independent task (Figure 2)
was used for this task. Table 2 shows the recognition rate
achieved on each database in the cross-database case and it
also compares the results with other state-of-the-art meth-
ods. It can be seen that our method outperforms the state-
of-the-art results in CK+, FERA, and DISFA databases. On
MMI, our method does not show improvements comparing
to others (e.g. [62]). However, authors in [62] trained their
classifier only with CK+ database while our method uses in-
stances from two additional databases (DISFA and FERA)
with completely different settings and subjects which add
significant amount of ambiguity in the training phase.
In order to have a fair comparison with other methods,
we provide the different settings used by the works men-
tioned in Table 2. The results provided in [34] are achieved
by training the models on one of the CK+, MMI, and FEED-
TUM databases and tested on the rest. The reported result
in [47] is the best achieved results using different SVM ker-
nels trained on CK+ and tested on MMI database. In [35]
several experiments were performed using four classifiers
(SVM, Nearest Mean Classifier, Weighted Template Match-
ing, and K-nearest neighbors). The reported results in this
work for CK+ is trained on MMI and Jaffe databases while
the reported results for MMI is trained on the CK+ database
only. As mentioned earlier, in [62] a Multiple Kernel Learn-
ing algorithm is used and the cross-database experiments
are trained on CK+, evaluated on MMI and vice versa. In
[38] a DNN network is proposed using traditional Incep-
tion layer. The networks for the cross-database case in this
work are tested on either CK+, MultiPIE, MMI, DISFA,
FERA, SFEW, or FER2013 while trained on the rest. Some
8. state-of-the-art methods
2D
Inception-ResNet
3D
Inception-ResNet
3D
Inception-ResNet
+
landmarks
CK+
84.1 [34], 84.4 [28], 88.5 [54], 92.0 [29],
93.2 [38], 92.4 [30], 93.6 [62]
85.77 89.50 93.21±2.32
MMI
63.4 [30], 75.12 [31], 74.7 [29], 79.8 [54],
86.7 [47], 78.51 [36]
55.83 67.50 77.50±1.76
FERA 56.1 [30], 55.6 [57], 76.7 [38] 49.64 67.74 77.42±3.67
DISFA 55.0 [38] - 51.35 58.00±5.77
Table 1. Recognition rates (%) in subject-independent task
(a) CK+ (b) MMI
(c) FERA (d) DISFA
Figure 4. Confusion matrices of 3D Inception-ResNet with landmarks for subject-independent task
of the expressions of these databases are excluded in this
study (such as neutral, relief, and contempt). There are
other works that perform their experiments on action unit
recognition task [4, 14, 26] but since fair comparison of ac-
tion unit recognition and facial expression recognition is not
easily obtainable, we did not mention these works in Ta-
bles 1 and 2.
Figure 5 shows the resulting confusion matrices of our
experiments on 3D Inception-ResNet with landmarks in
cross-database task. For CK+ (Figure 5a), we exclude the
contempt sequences in the test phase since other databases
that are used for training the network, do not contain con-
tempt category. Except for the fear expression (which has
very few number of samples in other databases), the net-
work has been able to correctly recognize other expressions.
For MMI (Figure 5b), highest recognition rate belongs to
surprise while the lowest one belongs to fear. Also, we can
see high confusion rate in recognizing sadness. On FERA
(Figure 5c), we exclude relief category as other databases do
not contain this emotion. Considering the fact that only half
of the train categories exist in the test set, the network shows
acceptable performance in correctly recognizing emotions.
However, surprise category has made significant confusion
in all of the categories. On DISFA (Figure 5d), we exclude
the neutral category as other databases do not contain this
category. Highest recognition rates belong to happy and sur-
9. (a) CK+ (b) MMI
(c) FERA (d) DISFA
Figure 5. Confusion matrices of 3D Inception-ResNet with landmarks for cross-database task
prise emotions while lowest one belongs to fear. Comparing
to other databases, we can see a significant increase in con-
fusion rate in all of the categories. This can be in part due
to the fact that emotions in DISFA are “spontaneous” while
emotions in the training databases are “posed”. Based on
the aforementioned results, our method provides a compre-
hensive solution that can generalize well to practical appli-
cations.
5. Conclusion
In this paper, we presented a 3D Deep Neural Network
for the task of facial expression recognition in videos. We
state-of-the-art methods
3D
Inception-ResNet
+
landmarks
CK+
47.1 [34], 56.0 [35],
61.2 [62], 64.2 [38]
67.52
MMI
51.4 [34], 50.8 [47],
36.8 [35], 55.6 [38],
66.9 [62]
54.76
FERA 39.4[38] 41.93
DISFA 37.7 [38] 40.51
Table 2. Recognition rates (%) in cross-database task
proposed the 3D Inception-ResNet (3DIR) network which
extends the well-known 2D Inception-ResNet module for
processing image sequences. This additional dimension
will result in a volume of feature maps and will extract
the spatial relations between frames in a sequence. This
module is followed by an LSTM which takes these tempo-
ral relations into account and uses this information to clas-
sify the sequences. In order to differentiate between facial
components and other parts of the face, we incorporated fa-
cial landmarks in our proposed method. These landmarks
are multiplied with the input tensor in the residual module
which is replaced with the shortcuts in the traditional resid-
ual layer.
We evaluated our proposed method in subject-
independent and cross-database tasks. Four well-known
databases were used to evaluate the method: CK+, MMI,
FERA, and DISFA. Our experiments show that the pro-
posed method outperforms many of the state-of-the-art
methods in both tasks and provides a general solution for
the task of FER.
6. Acknowledgement
This work is partially supported by the NSF grants IIS-
1111568 and CNS-1427872. We gratefully acknowledge
the support from NVIDIA Corporation with the donation of
the Tesla K40 GPUs used for this research.
10. References
[1] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen,
C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al.
Tensorflow: Large-scale machine learning on heterogeneous
distributed systems. arXiv preprint arXiv:1603.04467, 2016.
5
[2] T. B¨anziger and K. R. Scherer. Introducing the geneva mul-
timodal emotion portrayal (gemep) corpus. Blueprint for af-
fective computing: A sourcebook, pages 271–294, 2010. 5
[3] W. Byeon, T. M. Breuel, F. Raue, and M. Liwicki. Scene
labeling with lstm recurrent neural networks. In Proceed-
ings of the IEEE Conference on Computer Vision and Pattern
Recognition, pages 3547–3555, 2015. 3
[4] W.-S. Chu, F. De la Torre, and J. F. Cohn. Modeling spatial
and temporal cues for multi-label facial action unit detection.
arXiv preprint arXiv:1608.00911, 2016. 7
[5] I. Cohen, N. Sebe, A. Garg, L. S. Chen, and T. S. Huang.
Facial expression recognition from video sequences: tempo-
ral and static modeling. Computer Vision and image under-
standing, 91(1):160–187, 2003. 2
[6] T. F. Cootes, G. J. Edwards, C. J. Taylor, et al. Active ap-
pearance models. IEEE Transactions on pattern analysis and
machine intelligence, 23(6):681–685, 2001. 2
[7] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Ac-
tive shape models-their training and application. Computer
vision and image understanding, 61(1):38–59, 1995. 2
[8] N. Dalal and B. Triggs. Histograms of oriented gradients for
human detection. In Computer Vision and Pattern Recogni-
tion, 2005. CVPR 2005. IEEE Computer Society Conference
on, volume 1, pages 886–893. IEEE, 2005. 2
[9] N. Dalal, B. Triggs, and C. Schmid. Human detection using
oriented histograms of flow and appearance. In European
conference on computer vision, pages 428–441. Springer,
2006. 2
[10] A. Damien et al. Tflearn. https://github.com/
tflearn/tflearn, 2016. 5
[11] A. Dhall, R. Goecke, S. Lucey, and T. Gedeon. Static fa-
cial expression analysis in tough conditions: Data, evalua-
tion protocol and benchmark. In Computer Vision Workshops
(ICCV Workshops), 2011 IEEE International Conference on,
pages 2106–2112. IEEE, 2011. 5
[12] J. Donahue, L. Anne Hendricks, S. Guadarrama,
M. Rohrbach, S. Venugopalan, K. Saenko, and T. Dar-
rell. Long-term recurrent convolutional networks for visual
recognition and description. In Proceedings of the IEEE
conference on computer vision and pattern recognition,
pages 2625–2634, 2015. 2, 3
[13] P. Ekman and W. V. Friesen. Constants across cultures in the
face and emotion. Journal of personality and social psychol-
ogy, 17(2):124, 1971. 1
[14] C. Fabian Benitez-Quiroz, R. Srinivasan, and A. M. Mar-
tinez. Emotionet: An accurate, real-time algorithm for the
automatic annotation of a million facial expressions in the
wild. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition, pages 5562–5570, 2016. 7
[15] Y. Fan, X. Lu, D. Li, and Y. Liu. Video-based emotion recog-
nition using cnn-rnn and c3d hybrid networks. In Proceed-
ings of the 18th ACM International Conference on Multi-
modal Interaction, ICMI 2016, pages 445–450, New York,
NY, USA, 2016. ACM. 2, 3
[16] W. V. Friesen and P. Ekman. Emfacs-7: Emotional facial
action coding system. Unpublished manuscript, University
of California at San Francisco, 2(36):1, 1983. 5
[17] I. J. Goodfellow, D. Erhan, P. L. Carrier, A. Courville,
M. Mirza, B. Hamner, W. Cukierski, Y. Tang, D. Thaler, D.-
H. Lee, et al. Challenges in representation learning: A report
on three machine learning contests. In International Con-
ference on Neural Information Processing, pages 117–124.
Springer, 2013. 5
[18] R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker.
Multi-pie. Image and Vision Computing, 28(5):807–813,
2010. 5
[19] B. Hasani, M. M. Arzani, M. Fathy, and K. Raahemifar.
Facial expression recognition with discriminatory graphical
models. In 2016 2nd International Conference of Signal Pro-
cessing and Intelligent Systems (ICSPIS), pages 1–7, Dec
2016. 2
[20] B. Hasani and M. H. Mahoor. Spatio-temporal facial expres-
sion recognition using convolutional neural networks and
conditional random fields. arXiv preprint arXiv:1703.06995,
2017. 2, 3, 6
[21] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning
for image recognition. In The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), June 2016. 2
[22] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in
deep residual networks. pages 630–645, 2016. 4
[23] S. Hochreiter and J. Schmidhuber. Long short-term memory.
Neural computation, 9(8):1735–1780, 1997. 2
[24] S. Ioffe and C. Szegedy. Batch normalization: Accelerating
deep network training by reducing internal covariate shift.
arXiv preprint arXiv:1502.03167, 2015. 2
[25] S. Jain, C. Hu, and J. K. Aggarwal. Facial expression
recognition with temporal modeling of shapes. In Computer
Vision Workshops (ICCV Workshops), 2011 IEEE Interna-
tional Conference on, pages 1642–1649. IEEE, 2011. 2
[26] S. Jaiswal and M. Valstar. Deep learning the dynamic ap-
pearance and shape of facial action units. In Applications of
Computer Vision (WACV), 2016 IEEE Winter Conference on,
pages 1–8. IEEE, 2016. 4, 7
[27] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet
classification with deep convolutional neural networks. In
Advances in neural information processing systems, pages
1097–1105, 2012. 2, 4
[28] S. H. Lee, K. N. K. Plataniotis, and Y. M. Ro. Intra-
class variation reduction using training expression images
for sparse representation based facial expression recognition.
IEEE Transactions on Affective Computing, 5(3):340–351,
2014. 7
[29] M. Liu, S. Li, S. Shan, and X. Chen. Au-aware deep
networks for facial expression recognition. In Automatic
Face and Gesture Recognition (FG), 2013 10th IEEE Inter-
national Conference and Workshops on, pages 1–6. IEEE,
2013. 7
11. [30] M. Liu, S. Li, S. Shan, R. Wang, and X. Chen. Deeply
learning deformable facial action parts model for dynamic
expression analysis. In Asian Conference on Computer Vi-
sion, pages 143–157. Springer, 2014. 7
[31] M. Liu, S. Shan, R. Wang, and X. Chen. Learning expres-
sionlets on spatio-temporal manifold for dynamic facial ex-
pression recognition. In Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition, pages 1749–
1756, 2014. 7
[32] P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and
I. Matthews. The extended cohn-kanade dataset (ck+): A
complete dataset for action unit and emotion-specified ex-
pression. In Computer Vision and Pattern Recognition Work-
shops (CVPRW), 2010 IEEE Computer Society Conference
on, pages 94–101. IEEE, 2010. 5
[33] S. M. Mavadati, M. H. Mahoor, K. Bartlett, P. Trinh, and J. F.
Cohn. Disfa: A spontaneous facial action intensity database.
IEEE Transactions on Affective Computing, 4(2):151–160,
2013. 5
[34] C. Mayer, M. Eggers, and B. Radig. Cross-database evalu-
ation for facial expression recognition. Pattern recognition
and image analysis, 24(1):124–132, 2014. 6, 7, 8
[35] Y.-Q. Miao, R. Araujo, and M. S. Kamel. Cross-domain
facial expression recognition using supervised kernel mean
matching. In Machine Learning and Applications (ICMLA),
2012 11th International Conference on, volume 2, pages
326–332. IEEE, 2012. 6, 8
[36] M. Mohammadi, E. Fatemizadeh, and M. H. Mahoor. Pca-
based dictionary building for accurate facial expression
recognition via sparse representation. Journal of Visual
Communication and Image Representation, 25(5):1082–
1092, 2014. 2, 7
[37] P. Molchanov, X. Yang, S. Gupta, K. Kim, S. Tyree, and
J. Kautz. Online detection and classification of dynamic hand
gestures with recurrent 3d convolutional neural network. In
Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition, pages 4207–4215, 2016. 2
[38] A. Mollahosseini, D. Chan, and M. H. Mahoor. Going deeper
in facial expression recognition using deep neural networks.
In 2016 IEEE Winter Conference on Applications of Com-
puter Vision (WACV), pages 1–10. IEEE, 2016. 1, 2, 6, 7,
8
[39] A. Mollahosseini, B. Hasani, M. J. Salvador, H. Abdollahi,
D. Chan, and M. H. Mahoor. Facial expression recognition
from world wild web. In The IEEE Conference on Com-
puter Vision and Pattern Recognition (CVPR) Workshops,
June 2016. 1, 2
[40] M. Pantic, M. Valstar, R. Rademaker, and L. Maat. Web-
based database for facial expression analysis. In 2005 IEEE
international conference on multimedia and Expo, pages 5–
pp. IEEE, 2005. 5
[41] S. Ren, X. Cao, Y. Wei, and J. Sun. Face alignment at 3000
fps via regressing local binary features. In Proceedings of the
IEEE Conference on Computer Vision and Pattern Recogni-
tion, pages 1685–1692, 2014. 4
[42] C. Sagonas, E. Antonakos, G. Tzimiropoulos, S. Zafeiriou,
and M. Pantic. 300 faces in-the-wild challenge: Database
and results. Image and Vision Computing, 47:3–18, 2016. 4
[43] C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic.
A semi-automatic methodology for facial landmark annota-
tion. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition Workshops, pages 896–903,
2013. 4
[44] E. Sariyanidi, H. Gunes, and A. Cavallaro. Automatic anal-
ysis of facial affect: A survey of registration, representation,
and recognition. IEEE transactions on pattern analysis and
machine intelligence, 37(6):1113–1133, 2015. 2
[45] N. Sebe, M. S. Lew, Y. Sun, I. Cohen, T. Gevers, and T. S.
Huang. Authentic facial expression analysis. Image and Vi-
sion Computing, 25(12):1856–1863, 2007. 2
[46] C. Shan, S. Gong, and P. W. McOwan. Dynamic facial
expression recognition using a bayesian temporal manifold
model. In BMVC, pages 297–306. Citeseer, 2006. 2
[47] C. Shan, S. Gong, and P. W. McOwan. Facial expression
recognition based on local binary patterns: A comprehensive
study. Image and Vision Computing, 27(6):803–816, 2009.
1, 2, 6, 7, 8
[48] C. Sminchisescu, A. Kanaujia, and D. Metaxas. Conditional
models for contextual human motion recognition. Computer
Vision and Image Understanding, 104(2):210–220, 2006. 2
[49] S. Song and J. Xiao. Deep sliding shapes for amodal 3d ob-
ject detection in rgb-d images. In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition,
pages 808–816, 2016. 2
[50] Y. Sun, X. Chen, M. Rosato, and L. Yin. Tracking vertex flow
and model adaptation for three-dimensional spatiotemporal
face analysis. Systems, Man and Cybernetics, Part A: Sys-
tems and Humans, IEEE Transactions on, 40(3):461–474,
2010. 2
[51] C. Szegedy, S. Ioffe, and V. Vanhoucke. Inception-v4,
inception-resnet and the impact of residual connections on
learning. arXiv preprint arXiv:1602.07261, 2016. 2, 3
[52] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed,
D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich.
Going deeper with convolutions. In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition,
pages 1–9, 2015. 2, 3
[53] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna.
Rethinking the inception architecture for computer vision.
In The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), June 2016. 2
[54] S. Taheri, Q. Qiu, and R. Chellappa. Structure-preserving
sparse decomposition for facial expression analysis. IEEE
Transactions on Image Processing, 23(8):3590–3603, 2014.
7
[55] Y. Tian, T. Kanade, and J. F. Cohn. Recognizing lower face
action units for facial expression analysis. In Automatic Face
and Gesture Recognition, 2000. Proceedings. Fourth IEEE
International Conference on, pages 484–490. IEEE, 2000. 1
[56] M. F. Valstar, B. Jiang, M. Mehu, M. Pantic, and K. Scherer.
The first facial expression recognition and analysis chal-
lenge. In Automatic Face & Gesture Recognition and Work-
shops (FG 2011), 2011 IEEE International Conference on,
pages 921–926. IEEE, 2011. 5
12. [57] M. F. Valstar, B. Jiang, M. Mehu, M. Pantic, and K. Scherer.
The first facial expression recognition and analysis chal-
lenge. In Automatic Face & Gesture Recognition and Work-
shops (FG 2011), 2011 IEEE International Conference on,
pages 921–926. IEEE, 2011. 7
[58] S. B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian,
and T. Darrell. Hidden conditional random fields for ges-
ture recognition. In Computer Vision and Pattern Recogni-
tion, 2006 IEEE Computer Society Conference on, volume 2,
pages 1521–1527. IEEE, 2006. 2
[59] Z. Wang and Z. Ying. Facial expression recognition based on
local phase quantization and sparse representation. In Natu-
ral Computation (ICNC), 2012 Eighth International Confer-
ence on, pages 222–225. IEEE, 2012. 2
[60] M. Yeasin, B. Bullot, and R. Sharma. Recognition of fa-
cial expressions and measurement of levels of interest from
video. Multimedia, IEEE Transactions on, 8(3):500–508,
2006. 2
[61] L. Yu. face-alignment-in-3000fps. https://github.
com/yulequan/face-alignment-in-3000fps,
2016. 4
[62] X. Zhang, M. H. Mahoor, and S. M. Mavadati. Facial expres-
sion recognition using {l} {p}-norm mkl multiclass-svm.
Machine Vision and Applications, 26(4):467–483, 2015. 6,
7, 8
[63] Y. Zhang and Q. Ji. Active and dynamic information fusion
for facial expression understanding from image sequences.
Pattern Analysis and Machine Intelligence, IEEE Transac-
tions on, 27(5):699–714, 2005. 2
[64] Y. Zhu, L. C. De Silva, and C. C. Ko. Using moment in-
variants and hmm in facial expression recognition. Pattern
Recognition Letters, 23(1):83–91, 2002. 2
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