This document provides an overview of a face recognition system that uses artificial neural networks. It describes the structure and processing of artificial neural networks, including convolutional networks. It discusses how the system works, including local image sampling, the self-organizing map, and the convolutional network. It then provides details about the implementation and applications of the system for face recognition, and concludes by discussing the benefits of the system.
Detection and recognition of face using neural networkSmriti Tikoo
This document describes research on face detection and recognition using neural networks. It discusses using the Viola-Jones algorithm for face detection and a backpropagation neural network for face recognition. The Viola-Jones algorithm uses haar features, integral images, AdaBoost training, and cascading classifiers for real-time face detection. A backpropagation network with sigmoid activation functions is trained on facial images for recognition. Results show the network can accurately recognize faces after training. The document concludes the approach allows face recognition from an input image and discusses limitations and potential improvements.
This document summarizes a seminar presentation on face recognition using neural networks. It discusses face recognition, neural networks, the steps involved which include pre-processing, principle component analysis, and back propagation neural networks. Advantages of neural networks for face recognition are robustness to variations in faces and ability to learn from data. Face recognition has applications in security and identification.
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 summarizes a student project to design software that can detect human faces in images. The project's objectives are outlined, including converting images to grayscale and using a Haar cascade classifier to detect faces. Implementation examples like Picasa and Facebook are provided. The procedure involves preprocessing the image, converting it to grayscale, loading face properties, and applying a detection algorithm to find faces. Limitations around orientation are noted, with plans to expand capabilities.
This document presents a human emotion recognition system that uses facial expression analysis to identify emotions. It discusses how emotions are important to human life and interaction. The system first captures images of a human face and preprocesses the images to extract features. It then compares the facial features to examples in a database to recognize the emotion based on distances between features. The system can identify six basic emotions with up to 97% accuracy. Limitations and potential to incorporate fuzzy logic for improved classification are also discussed.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
Detection and recognition of face using neural networkSmriti Tikoo
This document describes research on face detection and recognition using neural networks. It discusses using the Viola-Jones algorithm for face detection and a backpropagation neural network for face recognition. The Viola-Jones algorithm uses haar features, integral images, AdaBoost training, and cascading classifiers for real-time face detection. A backpropagation network with sigmoid activation functions is trained on facial images for recognition. Results show the network can accurately recognize faces after training. The document concludes the approach allows face recognition from an input image and discusses limitations and potential improvements.
This document summarizes a seminar presentation on face recognition using neural networks. It discusses face recognition, neural networks, the steps involved which include pre-processing, principle component analysis, and back propagation neural networks. Advantages of neural networks for face recognition are robustness to variations in faces and ability to learn from data. Face recognition has applications in security and identification.
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 summarizes a student project to design software that can detect human faces in images. The project's objectives are outlined, including converting images to grayscale and using a Haar cascade classifier to detect faces. Implementation examples like Picasa and Facebook are provided. The procedure involves preprocessing the image, converting it to grayscale, loading face properties, and applying a detection algorithm to find faces. Limitations around orientation are noted, with plans to expand capabilities.
This document presents a human emotion recognition system that uses facial expression analysis to identify emotions. It discusses how emotions are important to human life and interaction. The system first captures images of a human face and preprocesses the images to extract features. It then compares the facial features to examples in a database to recognize the emotion based on distances between features. The system can identify six basic emotions with up to 97% accuracy. Limitations and potential to incorporate fuzzy logic for improved classification are also discussed.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
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.
This document provides an overview of image processing. It defines image processing as any form of signal processing where the input is an image, such as photos or video frames, and the output can be another image or parameters related to the image. The document discusses applications of image processing like face detection and medical imaging. It also outlines different types of image processing, components used in image processing systems, and the future potential of image processing with more powerful computing. In conclusion, the document states that image processing techniques can enhance, analyze, and construct images for various applications.
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.
Face detection and recognition using surveillance camera2 editedSantu Chall
The document discusses face detection and recognition technology using surveillance cameras. It describes how face recognition systems work by detecting 80 nodal points on the face and creating a "face print" code based on distance measurements. The document outlines general face recognition steps including face detection, normalization, and identification. It discusses advantages like convenience and passive identification, and disadvantages like inability to distinguish identical twins. Potential applications described include security, law enforcement, immigration, and banking. The document proposes a 12-week project plan to develop a face recognition system prototype.
Face recognization using artificial nerual networkDharmesh Tank
This document presents an overview of face recognition using artificial neural networks. It discusses the basic concepts of face recognition, issues with existing systems, and proposes a new system using discrete cosine transform (DCT) for feature extraction and an artificial neural network with backpropagation for classification. DCT is used to extract illumination invariant features and reduce dimensionality. The neural network is trained on these features to recognize faces. Thresholding rules are also introduced to improve recognition performance. Real-time applications of face recognition like Microsoft's Project Natal are mentioned.
This document presents information on face detection techniques. It discusses image segmentation as a preprocessing step for face detection. Some common segmentation methods are thresholding, edge-based segmentation, and region-based segmentation. Face detection can be classified as implicit/pattern-based or explicit/knowledge-based. Implicit methods use techniques like templates, PCA, LDA, and neural networks, while explicit methods exploit cues like color, motion, and facial features. One method discussed is human skin color-based face detection, which filters for skin-colored regions and finds facial parts within those regions. Advantages include speed and independence from training data, while disadvantages include sensitivity to lighting and accessories.
1. The document discusses face recognition using an eigenface approach, which uses principal component analysis to extract features from a database of faces to generate eigenfaces that can be used to identify unknown faces.
2. The eigenface approach takes into account the entire face for recognition and is relatively insensitive to small changes in faces. It is faster, simpler, and has better learning capabilities compared to other approaches.
3. Some limitations are that accuracy is affected if lighting and face position vary greatly, it only works with grayscale images, and noisy or partially occluded faces decrease recognition performance.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
This document summarizes a student project on face detection and recognition. The project used OpenCV with Python to detect faces in images and video in real-time. It extracts Haar features and compares them to a training database to recognize faces. The system was able to identify multiple faces with reasonable accuracy, though performance decreased with head tilts or low image quality. Future work could improve robustness to disguises and add emotion or gender analysis.
Object detection is a computer vision technique that identifies objects in images and videos. It can detect things like faces, humans, buildings, and cars. Object detection has applications in areas like image retrieval, video surveillance, and face detection. Image processing techniques are used to both improve images for human interpretation and to make images more suitable for machine perception. These techniques include enhancing edges, converting images to binary, greyscale, or true color formats. Face detection is a common application that finds faces in images and ignores other objects. It is often used as the first step in face recognition systems.
The document describes a vehicle detection system using a fully convolutional regression network (FCRN). The FCRN is trained on patches from aerial images to predict a density map indicating vehicle locations. The proposed system is evaluated on two public datasets and achieves higher precision and recall than comparative shallow and deep learning methods for vehicle detection in aerial images. The system could help with applications like urban planning and traffic management.
Face Recognition Methods based on Convolutional Neural NetworksElaheh Rashedi
Convolutional neural networks (CNN) have improved the state of the art in many applications, especially the face recognition area. In this work, we present a review on latest face verification techniques based on Convolutional Neural Networks. In addition, we give a comparison on these techniques regarding their architecture, depth level, number of parameters in the network, and the obtained accuracy in identification and/or verification. Furthermore, as the availability of large-scale training dataset has significantly affected the performance of CNN based recognition methods, we present a preface to the most common large-scale face datasets, and then we describe some of the successful automatic data collection procedures.
Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
This document summarizes a student presentation on a face recognition lecture attendance system. The system uses image processing and comparison to recognize students' faces from a high-definition camera feed and compare them to a database to take attendance. It is controlled by the faculty member, who instructs the system to start and end recording. The system is intended to smartly track that students remain for the entire lecture session and also function as surveillance. At the end, it reverts a full attendance report back to a central database. Diagrams including class, activity, sequence and use case diagrams are also presented to depict the system workflow and actors.
The document discusses object recognition in computer vision. It begins with an overview of object recognition, describing it as the task of finding and identifying objects in images. It then discusses several specific applications of object recognition, including fingerprint recognition and license plate recognition. Fingerprint recognition involves extracting features called minutiae from fingerprint images, which are ridge endings and bifurcations. License plate recognition uses an ALPR system to segment character images, normalize them, and recognize the characters.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
Deep learning on face recognition (use case, development and risk)Herman Kurnadi
1) Face recognition using deep learning methods has achieved high accuracy, nearing and sometimes surpassing human-level performance on some datasets.
2) The document outlines the key steps in face recognition systems using deep learning: face detection, alignment, feature extraction, and recognition. It discusses several influential deep learning models that have improved accuracy.
3) Applications discussed include security, health, and marketing/retail uses. Concerns about bias and privacy are also mentioned.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
This document summarizes a student project on handwriting recognition of Hindi numerals using a Self-Organizing Map module. The student developed an approach using SOM for Hindi numeral recognition to increase accuracy of recognized letters. SOM is an artificial neural network that can recognize patterns through learning. It was trained on a dataset of Hindi numerals to classify input patterns. Evaluation showed the system achieved the goal of increased recognition accuracy of Hindi numerals.
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.
This document provides an overview of image processing. It defines image processing as any form of signal processing where the input is an image, such as photos or video frames, and the output can be another image or parameters related to the image. The document discusses applications of image processing like face detection and medical imaging. It also outlines different types of image processing, components used in image processing systems, and the future potential of image processing with more powerful computing. In conclusion, the document states that image processing techniques can enhance, analyze, and construct images for various applications.
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.
Face detection and recognition using surveillance camera2 editedSantu Chall
The document discusses face detection and recognition technology using surveillance cameras. It describes how face recognition systems work by detecting 80 nodal points on the face and creating a "face print" code based on distance measurements. The document outlines general face recognition steps including face detection, normalization, and identification. It discusses advantages like convenience and passive identification, and disadvantages like inability to distinguish identical twins. Potential applications described include security, law enforcement, immigration, and banking. The document proposes a 12-week project plan to develop a face recognition system prototype.
Face recognization using artificial nerual networkDharmesh Tank
This document presents an overview of face recognition using artificial neural networks. It discusses the basic concepts of face recognition, issues with existing systems, and proposes a new system using discrete cosine transform (DCT) for feature extraction and an artificial neural network with backpropagation for classification. DCT is used to extract illumination invariant features and reduce dimensionality. The neural network is trained on these features to recognize faces. Thresholding rules are also introduced to improve recognition performance. Real-time applications of face recognition like Microsoft's Project Natal are mentioned.
This document presents information on face detection techniques. It discusses image segmentation as a preprocessing step for face detection. Some common segmentation methods are thresholding, edge-based segmentation, and region-based segmentation. Face detection can be classified as implicit/pattern-based or explicit/knowledge-based. Implicit methods use techniques like templates, PCA, LDA, and neural networks, while explicit methods exploit cues like color, motion, and facial features. One method discussed is human skin color-based face detection, which filters for skin-colored regions and finds facial parts within those regions. Advantages include speed and independence from training data, while disadvantages include sensitivity to lighting and accessories.
1. The document discusses face recognition using an eigenface approach, which uses principal component analysis to extract features from a database of faces to generate eigenfaces that can be used to identify unknown faces.
2. The eigenface approach takes into account the entire face for recognition and is relatively insensitive to small changes in faces. It is faster, simpler, and has better learning capabilities compared to other approaches.
3. Some limitations are that accuracy is affected if lighting and face position vary greatly, it only works with grayscale images, and noisy or partially occluded faces decrease recognition performance.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
This document summarizes a student project on face detection and recognition. The project used OpenCV with Python to detect faces in images and video in real-time. It extracts Haar features and compares them to a training database to recognize faces. The system was able to identify multiple faces with reasonable accuracy, though performance decreased with head tilts or low image quality. Future work could improve robustness to disguises and add emotion or gender analysis.
Object detection is a computer vision technique that identifies objects in images and videos. It can detect things like faces, humans, buildings, and cars. Object detection has applications in areas like image retrieval, video surveillance, and face detection. Image processing techniques are used to both improve images for human interpretation and to make images more suitable for machine perception. These techniques include enhancing edges, converting images to binary, greyscale, or true color formats. Face detection is a common application that finds faces in images and ignores other objects. It is often used as the first step in face recognition systems.
The document describes a vehicle detection system using a fully convolutional regression network (FCRN). The FCRN is trained on patches from aerial images to predict a density map indicating vehicle locations. The proposed system is evaluated on two public datasets and achieves higher precision and recall than comparative shallow and deep learning methods for vehicle detection in aerial images. The system could help with applications like urban planning and traffic management.
Face Recognition Methods based on Convolutional Neural NetworksElaheh Rashedi
Convolutional neural networks (CNN) have improved the state of the art in many applications, especially the face recognition area. In this work, we present a review on latest face verification techniques based on Convolutional Neural Networks. In addition, we give a comparison on these techniques regarding their architecture, depth level, number of parameters in the network, and the obtained accuracy in identification and/or verification. Furthermore, as the availability of large-scale training dataset has significantly affected the performance of CNN based recognition methods, we present a preface to the most common large-scale face datasets, and then we describe some of the successful automatic data collection procedures.
Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
This document summarizes a student presentation on a face recognition lecture attendance system. The system uses image processing and comparison to recognize students' faces from a high-definition camera feed and compare them to a database to take attendance. It is controlled by the faculty member, who instructs the system to start and end recording. The system is intended to smartly track that students remain for the entire lecture session and also function as surveillance. At the end, it reverts a full attendance report back to a central database. Diagrams including class, activity, sequence and use case diagrams are also presented to depict the system workflow and actors.
The document discusses object recognition in computer vision. It begins with an overview of object recognition, describing it as the task of finding and identifying objects in images. It then discusses several specific applications of object recognition, including fingerprint recognition and license plate recognition. Fingerprint recognition involves extracting features called minutiae from fingerprint images, which are ridge endings and bifurcations. License plate recognition uses an ALPR system to segment character images, normalize them, and recognize the characters.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
Deep learning on face recognition (use case, development and risk)Herman Kurnadi
1) Face recognition using deep learning methods has achieved high accuracy, nearing and sometimes surpassing human-level performance on some datasets.
2) The document outlines the key steps in face recognition systems using deep learning: face detection, alignment, feature extraction, and recognition. It discusses several influential deep learning models that have improved accuracy.
3) Applications discussed include security, health, and marketing/retail uses. Concerns about bias and privacy are also mentioned.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
This document summarizes a student project on handwriting recognition of Hindi numerals using a Self-Organizing Map module. The student developed an approach using SOM for Hindi numeral recognition to increase accuracy of recognized letters. SOM is an artificial neural network that can recognize patterns through learning. It was trained on a dataset of Hindi numerals to classify input patterns. Evaluation showed the system achieved the goal of increased recognition accuracy of Hindi numerals.
Open CV Implementation of Object Recognition Using Artificial Neural Networksijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
UNIT I INTRODUCTION
Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of
ANNs-Supervised Learning Network.
Face Recognition Based Intelligent Door Control Systemijtsrd
This paper presents the intelligent door control system based on face detection and recognition. This system can avoid the need to control by persons with the use of keys, security cards, password or pattern to open the door. The main objective is to develop a simple and fast recognition system for personal identification and face recognition to provide the security system. Face is a complex multidimensional structure and needs good computing techniques for recognition. The system is composed of two main parts face recognition and automatic door access control. It needs to detect the face before recognizing the face of the person. In face detection step, Viola Jones face detection algorithm is applied to detect the human face. Face recognition is implemented by using the Principal Component Analysis PCA and Neural Network. Image processing toolbox which is in MATLAB 2013a is used for the recognition process in this research. The PIC microcontroller is used to automatic door access control system by programming MikroC language. The door is opened automatically for the known person according to the result of verification in the MATLAB. On the other hand, the door remains closed for the unknown person. San San Naing | Thiri Oo Kywe | Ni Ni San Hlaing ""Face Recognition Based Intelligent Door Control System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23893.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23893/face-recognition-based-intelligent-door-control-system/san-san-naing
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
PADDY CROP DISEASE DETECTION USING SVM AND CNN ALGORITHMIRJET Journal
- The document discusses a study on detecting diseases in paddy/rice crops using deep learning algorithms like convolutional neural networks (CNN) and support vector machines (SVM).
- A dataset of rice leaf images was created and a CNN model using transfer learning with MobileNet was developed and trained on the dataset to classify rice diseases.
- The proposed method aims to automatically classify rice disease images to help farmers more accurately identify diseases, as manual identification can be difficult and inaccurate. This could help improve treatment and support farmers.
The model explains how we can Automate System using Artificial Intelligence.
It broadly concerns about:-
1. Lane Detection.
2. Traffic Sign Classification.
3. Behavioural Cloning.
CONVOLUTIONAL NEURAL NETWORK BASED FEATURE EXTRACTION FOR IRIS RECOGNITION ijcsit
Iris is a powerful tool for reliable human identification. It has the potential to identify individuals with a
high degree of assurance. Extracting good features is the most significant step in the iris recognition
system. In the past, different features have been used to implement iris recognition system. Most of them are
depend on hand-crafted features designed by biometrics specialists. Due to the success of deep learning in
computer vision problems, the features learned by the Convolutional Neural Network (CNN) have gained
much attention to be applied for iris recognition system. In this paper, we evaluate the extracted learned
features from a pre-trained Convolutional Neural Network (Alex-Net Model) followed by a multi-class
Support Vector Machine (SVM) algorithm to perform classification. The performance of the proposed
system is investigated when extracting features from the segmented iris image and from the normalized iris
image. The proposed iris recognition system is tested on four public datasets IITD, iris databases CASIAIris-V1,
CASIA-Iris-thousand and, CASIA-Iris- V3 Interval. The system achieved excellent results with the
very high accuracy rate.
Iris is a powerful tool for reliable human identification. It has the potential to identify individuals with a high degree of assurance. Extracting good features is the most significant step in the iris recognition system. In the past, different features have been used to implement iris recognition system. Most of them are depend on hand-crafted features designed by biometrics specialists. Due to the success of deep learning in computer vision problems, the features learned by the Convolutional Neural Network (CNN) have gained much attention to be applied for iris recognition system. In this paper, we evaluate the extracted learned features from a pre-trained Convolutional Neural Network (Alex-Net Model) followed by a multi-class Support Vector Machine (SVM) algorithm to perform classification. The performance of the proposed system is investigated when extracting features from the segmented iris image and from the normalized iris image. The proposed iris recognition system is tested on four public datasets IITD, iris databases CASIAIris-V1, CASIA-Iris-thousand and, CASIA-Iris- V3 Interval. The system achieved excellent results with the very high accuracy rate.
Web Spam Classification Using Supervised Artificial Neural Network Algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
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.
With the technology development of medical industry, processing data is expanding rapidly and computation time
also increases due to many factors like 3D, 4D treatment planning, the increasing sophistication of MRI pulse
sequences and the growing complexity of algorithms. Graphics processing unit (GPU) addresses these problems
and gives the solutions for using their features such as, high computation throughput, high memory bandwidth,
support for floating-point arithmetic and low cost. Compute unified device architecture (CUDA) is a popular GPU
programming model introduced by NVIDIA for parallel computing. This review paper briefly discusses the need of
GPU CUDA computing in the medical image analysis. The GPU performances of existing algorithms are analyzed
and the computational gain is discussed. A few open issues, hardware configurations and optimization principles
of existing methods are discussed. This survey concludes the few optimization techniques with the medical imaging
algorithms on GPU. Finally, limitation and future scope of GPU programming are discussed.
Scene recognition using Convolutional Neural NetworkDhirajGidde
The document discusses scene recognition using convolutional neural networks. It begins with an abstract stating that scene recognition allows context for object recognition. While object recognition has improved due to large datasets and CNNs, scene recognition performance has not reached the same level of success. The document then discusses using a new scene-centric database called Places with over 7 million images to train CNNs for scene recognition. It establishes new state-of-the-art results on several scene datasets and allows visualization of network responses to show differences between object-centric and scene-centric representations.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
This document provides an overview of convolutional neural networks (CNNs). It explains that CNNs are a type of neural network that has been successfully applied to analyzing visual imagery. The document then discusses the motivation and biology behind CNNs, describes common CNN architectures, and explains the key operations of convolution, nonlinearity, pooling, and fully connected layers. It provides examples of CNN applications in computer vision tasks like image classification, object detection, and speech recognition. Finally, it notes several large tech companies that utilize CNNs for features like automatic tagging, photo search, and personalized recommendations.
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
IRJET- Real-Time Object Detection using Deep Learning: A SurveyIRJET Journal
This document summarizes recent advances in real-time object detection using deep learning. It first provides an overview of object detection and deep learning. It then reviews popular object detection models including CNNs, R-CNNs, Fast R-CNN, Faster R-CNN, YOLO, and SSD. The document proposes modifications to existing models to improve small object detection accuracy. Specifically, it proposes using Darknet-53 with feature map upsampling and concatenation at multiple scales to detect objects of different sizes. It also describes using k-means clustering to select anchor boxes tailored to each detection scale.
- Geoffrey Hinton gives a tutorial on deep belief nets and how to learn multi-layer generative models of unlabeled data by learning one layer of features at a time using restricted Boltzmann machines (RBMs).
- RBMs make it possible to efficiently learn deep generative models one layer at a time by approximating the intractable posterior distribution over hidden units given visible data.
- Layer-by-layer unsupervised pre-training of features followed by discriminative fine-tuning improves classification performance on benchmark datasets like MNIST compared to backpropagation alone.
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
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A Strategic Approach: GenAI in EducationPeter Windle
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1. PRESENTED BY:- GUIDED BY:-
SUMEET S. KAKANI PROF. V. M. UMALE
1
2. Introduction
What is an Artificial Neural Network?
ANN Structure
System Overview
Local image sampling
The Self Organizing-Map
Perceptron learning rule
Convolutional Network
System details
Implementation
Applications
Conclusion
References
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3. 1. Why face recognition?
Face Recognition systems enhance security, provide secure
access control, and protect personal privacy
Improvement in the performance and reliability of face
recognition
No need to remember any passwords or carry any ID
2. Why neural network?
Adaptive learning: An ability to learn how to do tasks
Self-Organization: An ANN can create its own organisation
Remarkable ability to derive meaning from complicated or
imprecise data
3
4. Definition: A Computing system made of a number of simple,
highly interconnected processing elements, which process
information by their dynamic state response to external input.
Motivated right from its inception by the recognition.
A machine that is designed to model the way in which the brain
performs a particular task.
A massively parallel distributed processor.
Resembles the brain in two respects:
1. Knowledge is acquired through a learning process.
2. Synaptic weights, are used to store the acquired knowledge.
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5. A Layer with n inputs xi.
correspondent weights Wji (i=1, 2, 3...n)
The function ∑ sums the n weighted inputs and passes
the result through a non-linear function φ(•), called
the activation function.
The function φ (•) processes the adding results plus a
threshold value θ, thus producing the output Y.
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6. a) Processing units
Receive input
The adjustment of the weights
Three types of units: 1. input units 2. hidden units 3. output units
During operation, units can be updated either synchronously or
asynchronously.
b) Connections between units
n
Y(t) = φ( ∑ wij(t) Xj(t) + θ)
j=1
c) Transfer Function:
The behavior of an ANN depends on both the weights and the
transfer function that is specified for the units.
This function typically falls into one of two categories:
Linear : the output activity is proportional to the total weighted
output.
Threshold: the output is set at one of two levels, depending on
whether the total input is greater than or less than some threshold
value. 6
7. Following are the basic processes that are used by the system
to capture and compare images:
1. Detection:
Recognition software searches the field of view of a video
camera for faces.
Once the face is in view, it is detected within a fraction of a
second .
2. Alignment:
Once a face is detected, the head's position, size and pose
is the first thing that is determined.
3. Normalization:
The image of the head is scaled and rotated so that it can be
registered and mapped into an appropriate size and pose.
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8. 4. Representation:
Translation of facial data into unique code is done
by the system.
5. Matching:
The newly acquired facial data is compared to the
stored data and (ideally) linked to at least one
stored facial representation
This includes-
Local image sampling
The Self Organizing-Map
Convolutional Network
8
9. Figure : A representation of the local image sampling process.
• We have evaluated two different methods of representing
local image samples.
• In each method a window is scanned over the image as
shown in figure.
9
10. Used to reduce the dimensions of the image vector
Self-Organizing Map(SOM), is an unsupervised learning
process, which learns the distribution of a set of patterns
without any class information.
Unsupervised learning:
a) No external teacher and is based upon only local information.
b) It is also referred to as self-organization unsupervised learning.
The basic SOM consists of:
I) A 2 dimensional lattice L of neurons.
II) Each neuron ni belongs to L has an associated codebook
vector μi belongs to Rn.
III) The lattice is either rectangular or hexagonal as shown in figure
10
11. Figure: Rectangular and hexagonal lattice
• Self-organizing maps learn both the distribution and topology of the
input vectors they are trained on.
• Here a self-organizing feature map network identifies a winning
neuron i using the same procedure as employed by a competitive
layer.
• However, instead of updating only the winning neuron, all neurons
within a certain neighborhood of the winning neuron are updated
11
12. Perceptrons are trained on examples of
desired behavior.
The desired behavior can be summarized by a set of input,
output pairs as:
p1t1, p2t2,………….. pQtQ
Where p is an input to the network and t is the corresponding
correct (target) output.
The perceptron learning rule can be written more succinctly in
terms of the error e = t - a, and the change to be made to the
weight vector Δw:
Case 1: If e = 0, then make a change Δw equal to 0.
Case 2: If e = 1, then make a change Δw equal to p T.
Case 3: If e = -1, then make a change Δw equal to -p T.
12
14. Automatically synthesize simple problem specific
feature extractor from training data.
Feature detectors applied everywhere.
Features get progressively more global and invariant.
The whole system is trained “end-to-end” with
gradient based method to minimize a global loss
function.
Integrate segmentation, feature extraction, and
invariant classification in one stretch.
14
15. 1. Convolutional Mechanism
Capable of extracting similar features in different
places in the image.
Shifting the input only shifts the feature map
(robust to shift).
2. Subsampling Mechanism
The exact positions of the extracted features
are not important.
Only relative position of a feature to another
feature is relevant.
Reduce spatial resolution – Reduce sensitivity
to shift and distortion.
15
16. For the images in the training set, a fixed size window is
stepped over the entire image.
As shown in earlier figure and local image samples are
extracted at each step.
A self-organizing map is trained on the vectors from the
previous stage
16
17. The same window as in the first step is stepped over all
of the images in the training and test sets.
The local image samples are passed through the SOM
at each step, thereby creating new training and test
sets in the output space created by the self-organizing
map.
A convolutional neural network is trained on the
newly created training set
17
19. Law Enforcement: Minimizing victim trauma by narrowing mug
shot searches, Verifying identify for court records, and
comparing school Surveillance camera Images to known child
molesters.
Security/Counter terrorism: Access control, comparing
surveillance images to Known terrorists.
Day Care: Verify identity of individuals picking up the children.
Missing Children/Runaways Search surveillance images and the
internet
for missing children and runaways.
Residential Security: Alert homeowners of approaching
personnel.
Healthcare: Minimize fraud by verifying identity.
Banking: Minimize fraud by verifying identity.
19
20. There are no explicit three-dimensional models
in our system, however we have found that the
quantized local image samples used as input
to the convolutional network represent
smoothly changing shading patterns.
Higher level features are constructed from
these building blocks in successive layers of the
convolutional network.
In comparison with the eigenfaces approach,
we believe that the system presented here is
able to learn more appropriate features in
order to provide improved generalization.
20
21. 1. Steve Lawrence, C. Lee Giles , “Face Recognition: A Convolutional Neural Network
Approach”, IEEE transaction, St. Lucia, Australia.
2. David a brown, Ian craw, Julian lewthwaite, “Interactive Face retrieval using self
organizing maps-A SOM based approach to skin detection with application in real
time systems”, IEEE 2008 conference, Berlin, Germany.
3. Shahrin Azuan Nazeer, Nazaruddin Omar' and Marzuki Khalid, “Face Recognition
System using Artificial Neural Networks Approach”, IEEE - ICSCN 2007, MIT Campus,
Anna University, Chennai, India. Feb. 22-24, 2007. pp.420-425.
4. M. Prakash and M. Narasimha Murty, “Recognition Methods and Their Neural-
Network Models”, IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 1, JANUARY
2005.
5. E. Oja, “Neural networks, principal components and subspaces”, Int. J. Neural Syst., vol.
1, pp. 61–68, 2004.
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