Optimization of Facial Landmark for Sentiment Analysis on Images with Human Faces.
•Download as DOCX, PDF•
0 likes•80 views
Facial landmarks involve localizing key features on human faces, such as eyes, nose, and mouth. This technique is used in applications like video surveillance, computer vision, and human-computer interaction. However, facial landmark detection remains challenging due to issues including varying image quality, pose, lighting, and face shape/size. The document discusses different algorithms that can be used for facial landmark detection, including histogram of oriented gradients (HOG) and linear support vector machines (SVM). HOG counts gradient orientations in localized image regions and is robust to lighting changes, making it efficient for face detection when combined with machine and deep learning approaches.
Report
Share
Report
Share
![Introduction
Facial Landmarks is the localization and detection of core features on the face. Facial
Landmarks involve the focus of an area of interest on the human face. Its application finds
various usage but not limited to these areas, such as video surveillance, computer vision,
human-computer interaction. It revolves across core subject areas in face detection, face
expression detection, face tracking. It is a subset of the shape prediction; shape prediction
is used to localize distinguished features in a shape. A shape prediction attempts to localize
key points of interest along with the shape [1]. Facial landmarks are used to localize and
represent the salient regions in the face such as eyebrows, eyes, nose, mouth, and jawline.
Despite, the fact that face landmark technology has found immense several applications
across different fields, it still remains one of the most challenging tasks to perform.
Challenges such as poor image quality, image orientation, pose variation, lighting condition
on images, a variation of face shapes and sizes. In video-based face recognition, several
issues take place such as illumination variation, different pose orientation. In different
algorithms, images of faces to be trained are stored in a database and compared to the input
facial image There is a need to make recognition reliable under an uncontrolled
environment where the images captured are likely to reduce quality and other useful details
due to various lighting conditions imperatives for the face recognition algorithm to function
properly. The variation of the output will immensely depend on this. Compromise viewing
condition makes this a challenging area. Facial Landmark is used in various computer
vision techniques such as face part extraction, face alignment, pose estimations, face
swapping, blink detection. The facial landmark algorithm is very specific to the kind of
problem and cannot be guaranteed to work unless it is applied and results are obtained.
Sequel to this, we have an implementation face analyzing algorithm to categorize human
face as happy or sad. Facial Landmark can be achieved by face detection and feature
extraction, it is done with the use of Histogram of Oriented Gradients (HOG) and Linear
Support Vector Machine (SVM) Algorithm. In this paper, our approach tends to leverage
on a compositional learning strategy of combining both algorithms. We use learning
machine learning for face detection and deep learning for feature extractions. In other
words, we use HOG + Linear SVM for feature detection and extraction of distinguishable
features with CNN.
Related Work
In the last decade, a great deal of work has been done on this topic, Barnabas et al [5]
developed a three-stage detection strategy that hierarchically integrates face- and facial
landmark candidates as marked by individual filters. The image is first scanned using multi-
resolution eye pair detectors’ to mark possible candidate face regions, which are then
further processed by full-face detectors to find the best matching candidate. Facial
landmarks are found using specific eye, nose and mouth filters applied to regions of
interests meeting average geometric constraints derived from a large set of face images.
The specific subsystem for face- and facial landmark detection employs filter banks and](https://image.slidesharecdn.com/optimizationoffaciallandmark-191218202439/85/Optimization-of-Facial-Landmark-for-Sentiment-Analysis-on-Images-with-Human-Faces-1-320.jpg)
![registers memory activations as the center of the retinal sampling grid move over a selected
region in the image. Consisting of four stages retinal sampling, feature extraction, visual
filter banks, and holistic detection/classification. The drawback of this method can be found
in the time-consuming scanning algorithm used to prime the facial landmarks, with purely
data-driven attention mechanisms, the inaccurate results yielded for a compromised image
quality input
Several algorithms can be used to combat facial landmarks among these are histogram of
oriented gradients, local binary pattern histogram, eigenfaces, Fisher faces. In the histogram
of oriented gradients (HOG), every pixel of the captured image is compared with other
pixels in darkness. Based on that, an accurate image is obtained. It is unaffected by any
kind of lighting problems. Hence, it's a less complicated and more efficient technique for
face detection, we tend to combine the machine learning and deep learning approach to
achieve this result.
Histogram of Oriented Gradients (HOG) is commonly implored as a shape predictor form
which by detecting objects or human face in computer vision and image processing. The
object search is based on the detection technique applied for the small images defined by
the sliding detector window that probes region by region of the original input image and its
scaled versions [2]. Histogram of Oriented Gradients is generally used along with Support
Vector Machine (SVM) classifiers. Which handles the detecting the bounding box is used
to convert the 2-dimensional representation of the image into spatial coordinates. The
techniques counts occurrences of gradient orientation in localized portions of an image.
The simplest method to do that is to use vertical and horizontal operators [3]:
𝐺_𝑥 (𝑦, 𝑥) = 𝑌(𝑦, 𝑥 + 1)– 𝑌(𝑦, 𝑥 − 1); 𝐺_𝑦 (𝑦, 𝑥) = 𝑌(𝑦 + 1, 𝑥)– 𝑌(𝑦 − 1, 𝑥) (1)
𝑌( 𝑦, 𝑥) - pixel intensity at coordinates 𝑥 and 𝑦
𝐺𝑥(𝑦, 𝑥) - horizontal gradient
𝐺𝑦(𝑦, 𝑥) - vertical gradient
Magnitude and phase of the gradient are determined as:
𝐺(𝑦, 𝑥) = √ 𝐺_𝑥(𝑥, 𝑦)2 + 𝐺_𝑦(𝑥, 𝑦)2 (2)
𝜃( 𝑦, 𝑥) = 𝑎𝑟𝑐𝑡𝑎𝑛(𝐺𝑥(𝑦, 𝑥)) (3)
HOG is a good performer of human facial detection, however, it is very sensitive to image
rotation. Because the HOG shows occurrences of specific gradient orientation, the histogram
can be considerably changed by image rotation. Therefore, HOG is not suitable as feature
vectors for classification of objects which are often detected as rotated images or texture
images, hence the need to augment HOG with Linear SVM.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
J01116164
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 using pca and gabor with jaffe database 11748
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.
A017530114
This document summarizes a research paper that proposes an approach for face recognition under varying image resolutions using normalized feature representation and resolution mapping. The approach uses SVD for eigenface extraction and feature representation. It then proposes a novel method to optimize feature representation under different scales by developing scale-mapped features (SMF) that maximize correlation between original and scaled training features. A normalization approach is also presented to represent face features uniformly despite illumination and expression variations. Experimental results on the YALE face dataset show improved recognition accuracy under varying scaling conditions using this approach.
Face recognition across non uniform motion blur, illumination, and pose
This document summarizes a research paper that proposes a new methodology for face recognition in the presence of non-uniform (space-varying) motion blur. The key points are:
1) Existing face recognition methods cannot handle non-uniform blur that occurs in real-world camera shake situations.
2) The paper models blurred faces as a weighted combination of geometrically transformed versions of the original sharp image, using a "transformation spread function" to model non-uniform blur.
3) Algorithms are proposed to recognize faces under non-uniform blur alone, and also under combinations of blur, illumination changes, and pose variations. This is achieved by alternately estimating camera motion and illumination parameters.
Reconstruction of partially damaged facial image
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Study on Face Recognition Technique based on Eigenface
This document summarizes a study on face recognition techniques based on eigenfaces. It discusses principal component analysis (PCA) as a common approach for face recognition. PCA is used to extract principal components or eigenvectors from a collection of face images, which characterize variations between faces. These eigenvectors are called "eigenfaces" and can be used to encode and compare faces. The document evaluates PCA face recognition on four different image databases to test the robustness of the approach under different image types, formats, and compositions.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...
Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human-computer interaction and data-driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP features- which are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well-organized representation of face and is helpful in determining the resemblance among images.
A Hybrid Approach to Recognize Facial Image using Feature Extraction Method
This document proposes a hybrid approach for facial image recognition using feature extraction and classification methods. It will use Principal Component Analysis (PCA) for feature extraction to reduce the dimensionality of feature vectors and select the most important features. This will be followed by Support Vector Machine (SVM) classification to classify facial images. PCA is applied to eigenfaces derived from facial training images to form a feature space. Test images are projected into this space and classified by SVM based on distance between their eigenvectors and stored eigenvectors. The approach aims to improve classification accuracy over other methods by combining effective feature extraction and classification.
Recommended
J01116164
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 using pca and gabor with jaffe database 11748
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.
A017530114
This document summarizes a research paper that proposes an approach for face recognition under varying image resolutions using normalized feature representation and resolution mapping. The approach uses SVD for eigenface extraction and feature representation. It then proposes a novel method to optimize feature representation under different scales by developing scale-mapped features (SMF) that maximize correlation between original and scaled training features. A normalization approach is also presented to represent face features uniformly despite illumination and expression variations. Experimental results on the YALE face dataset show improved recognition accuracy under varying scaling conditions using this approach.
Face recognition across non uniform motion blur, illumination, and pose
This document summarizes a research paper that proposes a new methodology for face recognition in the presence of non-uniform (space-varying) motion blur. The key points are:
1) Existing face recognition methods cannot handle non-uniform blur that occurs in real-world camera shake situations.
2) The paper models blurred faces as a weighted combination of geometrically transformed versions of the original sharp image, using a "transformation spread function" to model non-uniform blur.
3) Algorithms are proposed to recognize faces under non-uniform blur alone, and also under combinations of blur, illumination changes, and pose variations. This is achieved by alternately estimating camera motion and illumination parameters.
Reconstruction of partially damaged facial image
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Study on Face Recognition Technique based on Eigenface
This document summarizes a study on face recognition techniques based on eigenfaces. It discusses principal component analysis (PCA) as a common approach for face recognition. PCA is used to extract principal components or eigenvectors from a collection of face images, which characterize variations between faces. These eigenvectors are called "eigenfaces" and can be used to encode and compare faces. The document evaluates PCA face recognition on four different image databases to test the robustness of the approach under different image types, formats, and compositions.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...
Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human-computer interaction and data-driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP features- which are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well-organized representation of face and is helpful in determining the resemblance among images.
A Hybrid Approach to Recognize Facial Image using Feature Extraction Method
This document proposes a hybrid approach for facial image recognition using feature extraction and classification methods. It will use Principal Component Analysis (PCA) for feature extraction to reduce the dimensionality of feature vectors and select the most important features. This will be followed by Support Vector Machine (SVM) classification to classify facial images. PCA is applied to eigenfaces derived from facial training images to form a feature space. Test images are projected into this space and classified by SVM based on distance between their eigenvectors and stored eigenvectors. The approach aims to improve classification accuracy over other methods by combining effective feature extraction and classification.
An Accurate Facial Component Detection Using Gabor Filter
Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial components
A04430105
This document provides a review of different techniques for facial expression recognition. It begins with an introduction to facial expression recognition and its applications. It then discusses common preprocessing steps like noise removal and illumination correction. Next, it examines popular feature extraction methods like Gabor filters, Log-Gabor filters, and Local Binary Patterns. Dimensionality reduction techniques like PCA and LDA are also covered. Finally, common classification algorithms such as SVM and KNN that are used to categorize facial expressions are described. The document concludes that combining feature extraction methods like Log-Gabor filters with dimensionality reduction and classification techniques can help improve performance and accuracy of facial expression recognition systems.
RATIONAL STRUCTURAL-ZERNIKE MEASURE: AN IMAGE SIMILARITY MEASURE FOR FACE REC...
Image similarity or image distortion assessment is the underlying technology in many computer vision applications, and is the root of many algorithms used in image processing. Many similarity measures have been proposed with the aim of achieving a high level of accuracy, and each of these measures has its strength as well as its weaknesses. In this paper, we present a highly efficient hybrid measure for image similarity that is based on structural and momental measures. We propose a similarity measure called the rational structural-Zernike measure (ZSM), to determine a reliable similarity between any two images including human faces images. This measure combines the best features of two structural measures, the well-known structural similarity index measure (SSIM) and the feature similarity index for image quality assessment (FSIM), with Zernike moments (ZMs), which have proven effective in the extraction of image features. Simulation results show that the proposed measure outperforms the SSIM, FSIM , ZMs and the state-of-art measure Feature-Based Structural Measure (FSM) through its ability to detect similarity even under distortion and to recognise the similarity between images of human faces under various conditions of facial expression and pose.
Facial Expression Recognition
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.
Facial Expression Recognition System Based on SVM and HOG Techniques
Facial expression is one of the most commonly used nonverbal means by humans to transmit internal emotional states and, therefore, it plays a fundamental role in interpersonal interactions. Although there is a wide range of possible facial expressions, psychologists have identified six fundamental ones (happiness, sadness, surprise, anger, fear and disgust) that are universally recognized. Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human–robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. The proposed work aims to design a robust facial expression recognition system (FER). FER system can be divided into three modules, namely facial registration, feature extraction and classification. The objective of this work is the recognition of facial expressions based the Histogram of Oriented Gradients (HOG) and support vector machine (SVM) algorithm.
A survey on human face recognition invariant to illumination
This document provides a survey of techniques for human face recognition that are invariant to illumination. It begins with an introduction to face recognition and the challenges posed by illumination variation. It then describes several preprocessing techniques that have been used to address illumination issues, including histogram equalization, adaptive histogram equalization, discrete cosine transform, discrete wavelet transform, and local binary pattern methods. The document concludes by comparing the performance of these various preprocessing techniques on standard face recognition databases based on error rates reported in other studies.
Ijarcet vol-2-issue-4-1352-1356
The document describes an algorithm for eye detection in face images. It begins with face detection using skin color detection in HSV color space. Then it finds the symmetric axis of the extracted face region using gradient orientation histograms to determine the location of the eyes. It further finds the symmetric axis within the eye region to locate the center of the eyes. The algorithm aims to accurately detect the eyes even when the face is rotated, which is important for applications like face recognition and gaze tracking.
IRJET- Prediction of Facial Attribute without Landmark Information
This document summarizes a research paper on predicting facial attributes without using landmark information. It describes the AFFAIR method which uses a global transformation network and part localization network to predict attributes. The global network learns an optimized transformation for each input face for attribute prediction. The part network localizes the most relevant facial regions for specific attributes. The global and local representations are then fused to predict attributes with high accuracy without requiring external landmark points. Experimental results on standard datasets show the AFFAIR method achieves state-of-the-art performance in landmark-free facial attribute prediction.
PARTIAL MATCHING FACE RECOGNITION METHOD FOR REHABILITATION NURSING ROBOTS BEDS
In order to establish face recognition system in rehabilitation nursing robots beds and achieve real-time
monitor the patient on the bed. We propose a face recognition method based on partial matching Hu
moments which apply for rehabilitation nursing robots beds. Firstly we using Haar classifier to detect
human faces automatically in dynamic video frames. Secondly we using Otsu threshold method to extract
facial features (eyebrows, eyes, mouth) in the face image and its Hu moments. Finally, we using Hu
moment feature set to achieve the automatic face recognition. Experimental results show that this method
can efficiently identify face in a dynamic video and it has high practical value (the accuracy rate is 91%
and the average recognition time is 4.3s).
IRJET- A Survey on Facial Expression Recognition Robust to Partial Occlusion
This document summarizes various approaches for facial expression recognition that are robust to partial facial occlusions. It begins by introducing the topic and importance of facial expression recognition systems that can handle real-world scenarios involving partial occlusions. It then categorizes and reviews key approaches in the literature, including feature reconstruction based on PCA or RPCA, sparse coding approaches using SRC or MLESR, sub-space based methods using Gabor filters or LGBPHS, and statistical prediction models using Bayesian or tracking methods. The document focuses on studies that have researched expression recognition for facial images with partial occlusions.
Ijetcas14 315
The document describes a novel method for localizing the pupil in eye gaze tracking systems. The proposed method fuses existing pupil localization techniques, including Hough transform, gray projection, and coarse positioning. This fusion approach aims to improve localization accuracy while lowering false detections. The method first preprocesses the eye image using filtering, edge detection, and thresholding/binarization. It then applies the fused techniques of Hough transform, gray projection, and coarse positioning to the preprocessed image to accurately detect the pupil boundary and center coordinates in a robust manner. The results are expected to provide better pupil localization compared to existing individual techniques.
H0334749
This document summarizes a research paper on face recognition using Gabor features and PCA. It begins with an introduction to face recognition and discusses challenges like lighting, pose, and orientation. It then describes how the proposed system uses Gabor wavelets for preprocessing to reduce variations from pose, lighting, etc. Principal component analysis (PCA) is used to extract low dimensional and discriminating feature vectors from the preprocessed images. These feature vectors are then used for classification with k-nearest neighbors. The proposed system was tested on the Yale face database containing 100 images of 10 subjects with variable illumination and expressions.
Hoip10 articulo reconocimiento facial_univ_vigo
Artículo presentado por la Universidad de Vigo durante la jornada HOIP'10 organizada por la Unidad de Sistemas de información e interacción de TECNALIA.
Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm
Face recognition across non uniform motion blur, illumination, and pose
The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.
Facial Feature Tracking under Varying Facial Expressions and Face Poses based...
This is the 2nd key paper: Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
For faces with expression
Multi modal face recognition using block based curvelet features
In this paper, we present multimodal 2D +3D face recognition method using block based curvelet features. The 3D surface of face (Depth Map) is computed from the stereo face images using stereo vision technique. The statistical measures such as mean, standard deviation, variance and entropy are extracted from each block of curvelet subband for both depth and intensity images independently.In order to compute the decision score, the KNN classifier is employed independently for both intensity and depth map. Further, computed decision scoresof intensity and depth map are combined at decision level to improve the face recognition rate. The combination of intensity and depth map is verified experimentally using benchmark face database. The experimental results show that the proposed multimodal method is better than individual modality.
Ijariie1177
This document reviews techniques for emotion recognition from facial expressions. It begins by outlining the general steps of emotion recognition systems as face detection, feature extraction, and classification. Popular techniques discussed include principal component analysis (PCA), local binary patterns (LBP), active appearance models, and Haar classifiers. PCA and LBP were found to provide higher recognition rates. The paper also reviews the Facial Action Coding System and compares the performance of different techniques based on recognition rate. In conclusion, PCA is identified as having the highest recognition rate and performance for emotion recognition.
Improved Face Recognition across Poses using Fusion of Probabilistic Latent V...
Uncontrolled environments have often required face recognition systems to identify faces
appearing in poses that are different from those of the enrolled samples. To address this problem,
probabilistic latent variable models have been used to perform face recognition across poses. Although
these models have demonstrated outstanding performance, it is not clear whether richer parameters
always lead to performance improvement. This work investigates this issue by comparing performance of
three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these
classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets
have shown that fusion of multiple classifiers improves face recognition across poses, given that the
individual classifiers have similar performance. This proves that different probabilistic latent variable
models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion
across multiple images has also been shown to produce better perfomance than recogition using single
still image.
Data Mining - Facial Expression Recognition
Automatic Facial Expression Recognition by employing motion vector features (deformation generated on a face during a facial expression) used as our data. Some of the most
state-of-the-art classification algorithms such as C5.0, CRT,
QUEST, CHAID, Deep Learning (DL), SVM and Discriminant
algorithms are used to classify the extracted motion vectors.
FACIAL LANDMARKING LOCALIZATION FOR EMOTION RECOGNITION USING BAYESIAN SHAPE ...
This work presents a framework for emotion recognition, based in facial expression analysis using Bayesian Shape Models (BSM) for facial landmarking localization. The Facial Action Coding System (FACS) compliant facial feature tracking based on Bayesian Shape Model. The BSM estimate the parameters of the model with an implementation of the EM algorithm. We describe the characterization methodology from parametric model and evaluated the accuracy for feature detection and estimation of the parameters associated with facial expressions, analyzing its robustness in pose and local variations. Then, a methodology for emotion characterization is introduced to perform the recognition. The experimental results show that the proposed model can effectively detect the different facial expressions. Outperforming conventional approaches for emotion recognition obtaining high performance results in the estimation of emotion present in a determined subject. The model used and characterizationmethodology showed efficient to detect the emotion type in 95.6% of the cases.
Report Face Detection
This document discusses face detection techniques. It begins with an introduction that defines face detection and discusses why it is important and challenging. It then covers topics like image segmentation, face detection approaches, morphological image processing, and skin color-based face detection. The document analyzes literature on face detection methods and provides descriptions of techniques like thresholding, edge detection, region-based segmentation, and template matching. It also includes a case study on specific face detection software applications and concludes by summarizing the discussed techniques.
Face recognition
This document summarizes a student project on face recognition. It begins with an introduction to face recognition, its applications, and common challenges. It then reviews literature on existing face recognition methods and identifies problems related to tilted poses and variations in illumination and expression. The proposed method will work to improve recognition rates under these conditions in two phases - training and testing. The method aims to enhance the preprocessing and feature extraction steps to make the system more robust. A basic flowchart of the proposed approach is provided, and the document concludes with references.
More Related Content
What's hot
An Accurate Facial Component Detection Using Gabor Filter
Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial components
A04430105
This document provides a review of different techniques for facial expression recognition. It begins with an introduction to facial expression recognition and its applications. It then discusses common preprocessing steps like noise removal and illumination correction. Next, it examines popular feature extraction methods like Gabor filters, Log-Gabor filters, and Local Binary Patterns. Dimensionality reduction techniques like PCA and LDA are also covered. Finally, common classification algorithms such as SVM and KNN that are used to categorize facial expressions are described. The document concludes that combining feature extraction methods like Log-Gabor filters with dimensionality reduction and classification techniques can help improve performance and accuracy of facial expression recognition systems.
RATIONAL STRUCTURAL-ZERNIKE MEASURE: AN IMAGE SIMILARITY MEASURE FOR FACE REC...
Image similarity or image distortion assessment is the underlying technology in many computer vision applications, and is the root of many algorithms used in image processing. Many similarity measures have been proposed with the aim of achieving a high level of accuracy, and each of these measures has its strength as well as its weaknesses. In this paper, we present a highly efficient hybrid measure for image similarity that is based on structural and momental measures. We propose a similarity measure called the rational structural-Zernike measure (ZSM), to determine a reliable similarity between any two images including human faces images. This measure combines the best features of two structural measures, the well-known structural similarity index measure (SSIM) and the feature similarity index for image quality assessment (FSIM), with Zernike moments (ZMs), which have proven effective in the extraction of image features. Simulation results show that the proposed measure outperforms the SSIM, FSIM , ZMs and the state-of-art measure Feature-Based Structural Measure (FSM) through its ability to detect similarity even under distortion and to recognise the similarity between images of human faces under various conditions of facial expression and pose.
Facial Expression Recognition
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.
Facial Expression Recognition System Based on SVM and HOG Techniques
Facial expression is one of the most commonly used nonverbal means by humans to transmit internal emotional states and, therefore, it plays a fundamental role in interpersonal interactions. Although there is a wide range of possible facial expressions, psychologists have identified six fundamental ones (happiness, sadness, surprise, anger, fear and disgust) that are universally recognized. Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human–robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. The proposed work aims to design a robust facial expression recognition system (FER). FER system can be divided into three modules, namely facial registration, feature extraction and classification. The objective of this work is the recognition of facial expressions based the Histogram of Oriented Gradients (HOG) and support vector machine (SVM) algorithm.
A survey on human face recognition invariant to illumination
This document provides a survey of techniques for human face recognition that are invariant to illumination. It begins with an introduction to face recognition and the challenges posed by illumination variation. It then describes several preprocessing techniques that have been used to address illumination issues, including histogram equalization, adaptive histogram equalization, discrete cosine transform, discrete wavelet transform, and local binary pattern methods. The document concludes by comparing the performance of these various preprocessing techniques on standard face recognition databases based on error rates reported in other studies.
Ijarcet vol-2-issue-4-1352-1356
The document describes an algorithm for eye detection in face images. It begins with face detection using skin color detection in HSV color space. Then it finds the symmetric axis of the extracted face region using gradient orientation histograms to determine the location of the eyes. It further finds the symmetric axis within the eye region to locate the center of the eyes. The algorithm aims to accurately detect the eyes even when the face is rotated, which is important for applications like face recognition and gaze tracking.
IRJET- Prediction of Facial Attribute without Landmark Information
This document summarizes a research paper on predicting facial attributes without using landmark information. It describes the AFFAIR method which uses a global transformation network and part localization network to predict attributes. The global network learns an optimized transformation for each input face for attribute prediction. The part network localizes the most relevant facial regions for specific attributes. The global and local representations are then fused to predict attributes with high accuracy without requiring external landmark points. Experimental results on standard datasets show the AFFAIR method achieves state-of-the-art performance in landmark-free facial attribute prediction.
PARTIAL MATCHING FACE RECOGNITION METHOD FOR REHABILITATION NURSING ROBOTS BEDS
In order to establish face recognition system in rehabilitation nursing robots beds and achieve real-time
monitor the patient on the bed. We propose a face recognition method based on partial matching Hu
moments which apply for rehabilitation nursing robots beds. Firstly we using Haar classifier to detect
human faces automatically in dynamic video frames. Secondly we using Otsu threshold method to extract
facial features (eyebrows, eyes, mouth) in the face image and its Hu moments. Finally, we using Hu
moment feature set to achieve the automatic face recognition. Experimental results show that this method
can efficiently identify face in a dynamic video and it has high practical value (the accuracy rate is 91%
and the average recognition time is 4.3s).
IRJET- A Survey on Facial Expression Recognition Robust to Partial Occlusion
This document summarizes various approaches for facial expression recognition that are robust to partial facial occlusions. It begins by introducing the topic and importance of facial expression recognition systems that can handle real-world scenarios involving partial occlusions. It then categorizes and reviews key approaches in the literature, including feature reconstruction based on PCA or RPCA, sparse coding approaches using SRC or MLESR, sub-space based methods using Gabor filters or LGBPHS, and statistical prediction models using Bayesian or tracking methods. The document focuses on studies that have researched expression recognition for facial images with partial occlusions.
Ijetcas14 315
The document describes a novel method for localizing the pupil in eye gaze tracking systems. The proposed method fuses existing pupil localization techniques, including Hough transform, gray projection, and coarse positioning. This fusion approach aims to improve localization accuracy while lowering false detections. The method first preprocesses the eye image using filtering, edge detection, and thresholding/binarization. It then applies the fused techniques of Hough transform, gray projection, and coarse positioning to the preprocessed image to accurately detect the pupil boundary and center coordinates in a robust manner. The results are expected to provide better pupil localization compared to existing individual techniques.
H0334749
This document summarizes a research paper on face recognition using Gabor features and PCA. It begins with an introduction to face recognition and discusses challenges like lighting, pose, and orientation. It then describes how the proposed system uses Gabor wavelets for preprocessing to reduce variations from pose, lighting, etc. Principal component analysis (PCA) is used to extract low dimensional and discriminating feature vectors from the preprocessed images. These feature vectors are then used for classification with k-nearest neighbors. The proposed system was tested on the Yale face database containing 100 images of 10 subjects with variable illumination and expressions.
Hoip10 articulo reconocimiento facial_univ_vigo
Artículo presentado por la Universidad de Vigo durante la jornada HOIP'10 organizada por la Unidad de Sistemas de información e interacción de TECNALIA.
Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm
Face recognition across non uniform motion blur, illumination, and pose
The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.
Facial Feature Tracking under Varying Facial Expressions and Face Poses based...
This is the 2nd key paper: Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines
For faces with expression
Multi modal face recognition using block based curvelet features
In this paper, we present multimodal 2D +3D face recognition method using block based curvelet features. The 3D surface of face (Depth Map) is computed from the stereo face images using stereo vision technique. The statistical measures such as mean, standard deviation, variance and entropy are extracted from each block of curvelet subband for both depth and intensity images independently.In order to compute the decision score, the KNN classifier is employed independently for both intensity and depth map. Further, computed decision scoresof intensity and depth map are combined at decision level to improve the face recognition rate. The combination of intensity and depth map is verified experimentally using benchmark face database. The experimental results show that the proposed multimodal method is better than individual modality.
Ijariie1177
This document reviews techniques for emotion recognition from facial expressions. It begins by outlining the general steps of emotion recognition systems as face detection, feature extraction, and classification. Popular techniques discussed include principal component analysis (PCA), local binary patterns (LBP), active appearance models, and Haar classifiers. PCA and LBP were found to provide higher recognition rates. The paper also reviews the Facial Action Coding System and compares the performance of different techniques based on recognition rate. In conclusion, PCA is identified as having the highest recognition rate and performance for emotion recognition.
Improved Face Recognition across Poses using Fusion of Probabilistic Latent V...
Uncontrolled environments have often required face recognition systems to identify faces
appearing in poses that are different from those of the enrolled samples. To address this problem,
probabilistic latent variable models have been used to perform face recognition across poses. Although
these models have demonstrated outstanding performance, it is not clear whether richer parameters
always lead to performance improvement. This work investigates this issue by comparing performance of
three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these
classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets
have shown that fusion of multiple classifiers improves face recognition across poses, given that the
individual classifiers have similar performance. This proves that different probabilistic latent variable
models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion
across multiple images has also been shown to produce better perfomance than recogition using single
still image.
Data Mining - Facial Expression Recognition
Automatic Facial Expression Recognition by employing motion vector features (deformation generated on a face during a facial expression) used as our data. Some of the most
state-of-the-art classification algorithms such as C5.0, CRT,
QUEST, CHAID, Deep Learning (DL), SVM and Discriminant
algorithms are used to classify the extracted motion vectors.
FACIAL LANDMARKING LOCALIZATION FOR EMOTION RECOGNITION USING BAYESIAN SHAPE ...
This work presents a framework for emotion recognition, based in facial expression analysis using Bayesian Shape Models (BSM) for facial landmarking localization. The Facial Action Coding System (FACS) compliant facial feature tracking based on Bayesian Shape Model. The BSM estimate the parameters of the model with an implementation of the EM algorithm. We describe the characterization methodology from parametric model and evaluated the accuracy for feature detection and estimation of the parameters associated with facial expressions, analyzing its robustness in pose and local variations. Then, a methodology for emotion characterization is introduced to perform the recognition. The experimental results show that the proposed model can effectively detect the different facial expressions. Outperforming conventional approaches for emotion recognition obtaining high performance results in the estimation of emotion present in a determined subject. The model used and characterizationmethodology showed efficient to detect the emotion type in 95.6% of the cases.
What's hot (20)
An Accurate Facial Component Detection Using Gabor Filter
An Accurate Facial Component Detection Using Gabor Filter
RATIONAL STRUCTURAL-ZERNIKE MEASURE: AN IMAGE SIMILARITY MEASURE FOR FACE REC...
RATIONAL STRUCTURAL-ZERNIKE MEASURE: AN IMAGE SIMILARITY MEASURE FOR FACE REC...
Facial Expression Recognition System Based on SVM and HOG Techniques
Facial Expression Recognition System Based on SVM and HOG Techniques
A survey on human face recognition invariant to illumination
A survey on human face recognition invariant to illumination
IRJET- Prediction of Facial Attribute without Landmark Information
IRJET- Prediction of Facial Attribute without Landmark Information
PARTIAL MATCHING FACE RECOGNITION METHOD FOR REHABILITATION NURSING ROBOTS BEDS
PARTIAL MATCHING FACE RECOGNITION METHOD FOR REHABILITATION NURSING ROBOTS BEDS
IRJET- A Survey on Facial Expression Recognition Robust to Partial Occlusion
IRJET- A Survey on Facial Expression Recognition Robust to Partial Occlusion
Face recognition across non uniform motion blur, illumination, and pose
Face recognition across non uniform motion blur, illumination, and pose
Facial Feature Tracking under Varying Facial Expressions and Face Poses based...
Facial Feature Tracking under Varying Facial Expressions and Face Poses based...
Multi modal face recognition using block based curvelet features
Multi modal face recognition using block based curvelet features
Improved Face Recognition across Poses using Fusion of Probabilistic Latent V...
Improved Face Recognition across Poses using Fusion of Probabilistic Latent V...
FACIAL LANDMARKING LOCALIZATION FOR EMOTION RECOGNITION USING BAYESIAN SHAPE ...
FACIAL LANDMARKING LOCALIZATION FOR EMOTION RECOGNITION USING BAYESIAN SHAPE ...
Similar to Optimization of Facial Landmark for Sentiment Analysis on Images with Human Faces.
Report Face Detection
This document discusses face detection techniques. It begins with an introduction that defines face detection and discusses why it is important and challenging. It then covers topics like image segmentation, face detection approaches, morphological image processing, and skin color-based face detection. The document analyzes literature on face detection methods and provides descriptions of techniques like thresholding, edge detection, region-based segmentation, and template matching. It also includes a case study on specific face detection software applications and concludes by summarizing the discussed techniques.
Face recognition
This document summarizes a student project on face recognition. It begins with an introduction to face recognition, its applications, and common challenges. It then reviews literature on existing face recognition methods and identifies problems related to tilted poses and variations in illumination and expression. The proposed method will work to improve recognition rates under these conditions in two phases - training and testing. The method aims to enhance the preprocessing and feature extraction steps to make the system more robust. A basic flowchart of the proposed approach is provided, and the document concludes with references.
A study of techniques for facial detection and expression classification
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
A Spectral Domain Local Feature Extraction Algorithm for Face Recognition
In this paper, a spectral domain feature extraction algorithm for face recognition is proposed, which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal bands from the face image. In order to capture the local variations within these high-informative horizontal bands precisely, a feature selection algorithm based on two-dimensional discrete Fourier transform (2D-DFT) is proposed. Magnitudes corresponding to the dominant 2D-DFT coefficients are selected as features and shown to provide high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentations have been carried out upon standard face databases and the recognition performance is compared with some of the existing face recognition schemes. It is found that the proposed method offers not only computational savings but also a very high degree of recognition accuracy.
50120140504002
This document summarizes a research paper that proposes a new face recognition method capable of recognizing faces with expressions, glasses, and/or rotation. The method uses variance estimation of the red, green, and blue color components to compare extracted faces to those in a database. It also uses Euclidean distance to compare extracted facial features (eyes, nose, mouth) to those in the database. The method is divided into three steps: 1) variance estimation of color components, 2) facial feature extraction based on feature locations, and 3) identifying similar faces by scanning the database. Experimental results showed the method achieved good accuracy, speed, and used simple computations for face recognition.
A Face Recognition Scheme Using Wavelet Based Dominant Features
In this paper, a multi-resolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal segments from the face image. In order to capture the local spatial variations within these highinformative horizontal bands precisely, the horizontal band is segmented into several small spatial modules. Dominant wavelet coefficients corresponding to each local region residing inside those horizontal bands are selected as features. In the selection of the dominant coefficients, a threshold criterion is proposed, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved by the proposed method in comparison to those obtained by some of the existing methods.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...
This document discusses facial expression recognition using local binary patterns and support vector machines. It begins by introducing facial expression recognition and its importance. It then describes preprocessing face images, detecting faces, extracting features using local binary patterns, and classifying expressions with support vector machines. Specifically, it details extracting LBP histograms from local regions of faces, concatenating them into a feature vector, and using an SVM for multi-class classification of expressions like happy, sad, angry, etc. Overall, the document provides an overview of the key steps involved in an automatic facial expression recognition system using LBP features and SVM classification.
C017431730
This document provides a literature review of face recognition techniques using face alignment and PCA. It discusses how face alignment techniques like Active Appearance Models (AAM) and Active Shape Models (ASM) are used to accurately align faces, which is important for face recognition. PCA is also discussed as a commonly used feature extraction and dimensionality reduction technique for face recognition. The document surveys recent research on face recognition using AAM for tasks like minimizing error between input and model images, modeling a wide range of facial appearances, and exploiting temporal correlations across image frames. It also discusses improvements to AAM modeling and fitting robustness.
A Study On Facial Expression Recognition Using Local Binary Pattern
This document summarizes a study on facial expression recognition using Local Binary Pattern (LBP) as the feature extraction technique. The study used the Japanese Female Facial Expressions (JAFFE) database to test LBP and Support Vector Machine (SVM) classification on seven basic facial expressions. LBP was shown to extract important facial features and achieved an average recognition accuracy of 89%, significantly outperforming without LBP at 67%. Angry, happy, and surprise expressions achieved over 90% accuracy, demonstrating the effectiveness of the LBP-SVM approach for facial expression recognition.
Semantic 3DTV Content Analysis and Description
Semantic 3DTV Content Analysis and DescriptionDistinguished Lecturer Series - Leon The Mathematician
Ioannis Pitas, Professor, Aristotle University of Thessaloniki, Department of Informatics (IEEE Fellow), Semantic 3DTV Content Analysis and DescriptionFace recogntion using PCA algorithm
This document discusses face recognition using the PCA algorithm. It begins with an introduction to face recognition and its challenges. It then provides background on face recognition techniques, including PCA. The document outlines an improved PCA (IPCA) algorithm that aims to address issues like orientation and lighting variations. It presents results of the IPCA algorithm on two test cases, showing it can accurately recognize faces even at 90 degree orientations. The document discusses advantages of face recognition but also limitations like sensitivity to expressions, lighting and angle. It raises privacy concerns about widespread use of facial recognition technology.
Intellectual Person Identification Using 3DMM, GPSO and Genetic Algorithm
Intellectual Person Identification Using 3DMM, GPSO and Genetic AlgorithmIJCSIS Research Publications
Three-dimensional multimodal models of objective classes are a great tool in modeling and recognition. The multimodal involuntary emotion recognition during a mentally challenged-based communication is presented. We have easily found the mentally disorder people without a doctor. The features are built upon the emotion, motion and frequency to identifying the percentage of mentally disorder peoples. Using Different categories of an image, video, audio and emotions can be discriminated. An image using an algorithms for classification is 3DMM (Three-dimensional morph able models) used to fit the model to images, and a framework for face emotion recognition. GPSO (Guided Particle Swarm Optimization) the emotion finding problem is basically an exploration problem, where at every point; we are pointed to recognize which of the thinkable emotions ensures the current facial expression denotes and GA (Genetic Algorithm) has the virtues of overflowing coding, and decoding, assigning complex information flexibly. GA is calculating the percentage of mental disorder. We proposed using different algorithm to identify the mentally challenged persons.Research and Development of DSP-Based Face Recognition System for Robotic Reh...
This article describes the development of DSP as the core of the face recognition system, on the basis of
understanding the background, significance and current research situation at home and abroad of face
recognition issue, having a in-depth study to face detection, Image preprocessing, feature extraction face
facial structure, facial expression feature extraction, classification and other issues during face recognition
and have achieved research and development of DSP-based face recognition system for robotic
rehabilitation nursing beds. The system uses a fixed-point DSP TMS320DM642 as a central processing
unit, with a strong processing performance, high flexibility and programmability.
IRJET- Pose Varying Face Recognition: Review
This document provides a review of techniques for pose varying face recognition. It begins by outlining some of the key challenges of face recognition across different poses, such as self-occlusion, loss of semantic correspondence, and nonlinear deformation of facial textures. It then categorizes and summarizes general face recognition algorithms as well as 2D and 3D techniques that have been developed to handle pose variations. Specifically, it reviews holistic and local approaches, real view-based matching, pose transformation techniques in both image and feature spaces, and 3D reconstruction methods. The document aims to compare existing approaches and identify promising directions for future research in pose invariant face recognition.
IRJET- Pose Varying Face Recognition: Review
This document provides a review of techniques for pose varying face recognition. It begins with an introduction explaining the importance and challenges of face recognition across different poses. It then categorizes and summarizes existing techniques into three classes: general face recognition algorithms, 2D techniques, and 3D techniques. For each class, it describes representative approaches and compares their advantages, disadvantages, and performance. The document concludes by suggesting promising areas for future research to further improve pose invariant face recognition.
PRE-PROCESSING TECHNIQUES FOR FACIAL EMOTION RECOGNITION SYSTEM
Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature extraction and classification technique for emotion recognition is still an open problem. Image pre-processing and normalization is significant part of face recognition systems. Changes in lighting conditions produces dramatically decrease of recognition performance. In this paper, the image pre-processing techniques like K-Nearest Neighbor, Cultural Algorithm and Genetic Algorithm are used to remove the noise in the facial image for enhancing the emotion recognition. The performance of the preprocessing techniques are evaluated with various performance metrics.
LEARNING BASES OF ACTICITY
The document discusses a proposed system for facial expression recognition using Gabor filters and linear basis functions. It involves detecting faces, extracting features using Gabor filters, classifying expressions using a linear model trained on Gabor phase shifts, and evaluating performance. The proposed system aims to address issues with existing representation learning methods and achieve state-of-the-art expression recognition results on both posed and spontaneous expressions.
Model Based Emotion Detection using Point Clouds
The document discusses challenges and approaches for facial emotion recognition. It aims to develop a model-based approach for real-time driver emotion recognition on an embedded platform using parallel processing. Model-based approaches can overcome issues like illumination and pose variations. The document reviews several state-of-the-art methods and discusses challenges like occlusion, lighting distortions, and complex backgrounds. It describes exploring both 2D and 3D techniques for facial feature extraction and expression recognition.
Real time facial expression analysis using pca
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.
Multi Local Feature Selection Using Genetic Algorithm For Face Identification
This document presents a face recognition algorithm that uses a multi-local feature selection approach based on genetic algorithms and pseudo Zernike moment invariants. The algorithm involves five stages: 1) face detection using an ellipse to approximate the face region, 2) extraction of facial features (eyes, nose, mouth) within regions using genetic algorithms to locate templates with maximum edge density, 3) generation of moment invariants from the facial features using pseudo Zernike polynomials, 4) classification of facial features using radial basis function neural networks, and 5) selection of multiple local features for face identification. The algorithm was tested on over 3000 images from three databases, achieving recognition rates over 89% which is higher than global or single local feature approaches and
Similar to Optimization of Facial Landmark for Sentiment Analysis on Images with Human Faces. (20)
A study of techniques for facial detection and expression classification
A study of techniques for facial detection and expression classification
A Spectral Domain Local Feature Extraction Algorithm for Face Recognition
A Spectral Domain Local Feature Extraction Algorithm for Face Recognition
A Face Recognition Scheme Using Wavelet Based Dominant Features
A Face Recognition Scheme Using Wavelet Based Dominant Features
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...
A Study On Facial Expression Recognition Using Local Binary Pattern
A Study On Facial Expression Recognition Using Local Binary Pattern
Intellectual Person Identification Using 3DMM, GPSO and Genetic Algorithm
Intellectual Person Identification Using 3DMM, GPSO and Genetic Algorithm
Research and Development of DSP-Based Face Recognition System for Robotic Reh...
Research and Development of DSP-Based Face Recognition System for Robotic Reh...
PRE-PROCESSING TECHNIQUES FOR FACIAL EMOTION RECOGNITION SYSTEM
PRE-PROCESSING TECHNIQUES FOR FACIAL EMOTION RECOGNITION SYSTEM
Multi Local Feature Selection Using Genetic Algorithm For Face Identification
Multi Local Feature Selection Using Genetic Algorithm For Face Identification
Recently uploaded
End-to-end pipeline agility - Berlin Buzzwords 2024
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.
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
原版定制【微信:41543339】【(Bradford毕业证书)布拉德福德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Dalhousie毕业证书)达尔豪斯大学毕业证如何办理
原版定制【微信:41543339】【(Dalhousie毕业证书)达尔豪斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
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
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
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
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
The Building Blocks of QuestDB, a Time Series Database
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.
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版制作(swinburne毕业证书)斯威本科技大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(swinburne毕业证书)斯威本科技大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Recently uploaded (20)
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
Influence of Marketing Strategy and Market Competition on Business Plan
Influence of Marketing Strategy and Market Competition on Business Plan
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Optimization of Facial Landmark for Sentiment Analysis on Images with Human Faces.
- 1. Introduction Facial Landmarks is the localization and detection of core features on the face. Facial Landmarks involve the focus of an area of interest on the human face. Its application finds various usage but not limited to these areas, such as video surveillance, computer vision, human-computer interaction. It revolves across core subject areas in face detection, face expression detection, face tracking. It is a subset of the shape prediction; shape prediction is used to localize distinguished features in a shape. A shape prediction attempts to localize key points of interest along with the shape [1]. Facial landmarks are used to localize and represent the salient regions in the face such as eyebrows, eyes, nose, mouth, and jawline. Despite, the fact that face landmark technology has found immense several applications across different fields, it still remains one of the most challenging tasks to perform. Challenges such as poor image quality, image orientation, pose variation, lighting condition on images, a variation of face shapes and sizes. In video-based face recognition, several issues take place such as illumination variation, different pose orientation. In different algorithms, images of faces to be trained are stored in a database and compared to the input facial image There is a need to make recognition reliable under an uncontrolled environment where the images captured are likely to reduce quality and other useful details due to various lighting conditions imperatives for the face recognition algorithm to function properly. The variation of the output will immensely depend on this. Compromise viewing condition makes this a challenging area. Facial Landmark is used in various computer vision techniques such as face part extraction, face alignment, pose estimations, face swapping, blink detection. The facial landmark algorithm is very specific to the kind of problem and cannot be guaranteed to work unless it is applied and results are obtained. Sequel to this, we have an implementation face analyzing algorithm to categorize human face as happy or sad. Facial Landmark can be achieved by face detection and feature extraction, it is done with the use of Histogram of Oriented Gradients (HOG) and Linear Support Vector Machine (SVM) Algorithm. In this paper, our approach tends to leverage on a compositional learning strategy of combining both algorithms. We use learning machine learning for face detection and deep learning for feature extractions. In other words, we use HOG + Linear SVM for feature detection and extraction of distinguishable features with CNN. Related Work In the last decade, a great deal of work has been done on this topic, Barnabas et al [5] developed a three-stage detection strategy that hierarchically integrates face- and facial landmark candidates as marked by individual filters. The image is first scanned using multi- resolution eye pair detectors’ to mark possible candidate face regions, which are then further processed by full-face detectors to find the best matching candidate. Facial landmarks are found using specific eye, nose and mouth filters applied to regions of interests meeting average geometric constraints derived from a large set of face images. The specific subsystem for face- and facial landmark detection employs filter banks and
- 2. registers memory activations as the center of the retinal sampling grid move over a selected region in the image. Consisting of four stages retinal sampling, feature extraction, visual filter banks, and holistic detection/classification. The drawback of this method can be found in the time-consuming scanning algorithm used to prime the facial landmarks, with purely data-driven attention mechanisms, the inaccurate results yielded for a compromised image quality input Several algorithms can be used to combat facial landmarks among these are histogram of oriented gradients, local binary pattern histogram, eigenfaces, Fisher faces. In the histogram of oriented gradients (HOG), every pixel of the captured image is compared with other pixels in darkness. Based on that, an accurate image is obtained. It is unaffected by any kind of lighting problems. Hence, it's a less complicated and more efficient technique for face detection, we tend to combine the machine learning and deep learning approach to achieve this result. Histogram of Oriented Gradients (HOG) is commonly implored as a shape predictor form which by detecting objects or human face in computer vision and image processing. The object search is based on the detection technique applied for the small images defined by the sliding detector window that probes region by region of the original input image and its scaled versions [2]. Histogram of Oriented Gradients is generally used along with Support Vector Machine (SVM) classifiers. Which handles the detecting the bounding box is used to convert the 2-dimensional representation of the image into spatial coordinates. The techniques counts occurrences of gradient orientation in localized portions of an image. The simplest method to do that is to use vertical and horizontal operators [3]: 𝐺_𝑥 (𝑦, 𝑥) = 𝑌(𝑦, 𝑥 + 1)– 𝑌(𝑦, 𝑥 − 1); 𝐺_𝑦 (𝑦, 𝑥) = 𝑌(𝑦 + 1, 𝑥)– 𝑌(𝑦 − 1, 𝑥) (1) 𝑌( 𝑦, 𝑥) - pixel intensity at coordinates 𝑥 and 𝑦 𝐺𝑥(𝑦, 𝑥) - horizontal gradient 𝐺𝑦(𝑦, 𝑥) - vertical gradient Magnitude and phase of the gradient are determined as: 𝐺(𝑦, 𝑥) = √ 𝐺_𝑥(𝑥, 𝑦)2 + 𝐺_𝑦(𝑥, 𝑦)2 (2) 𝜃( 𝑦, 𝑥) = 𝑎𝑟𝑐𝑡𝑎𝑛(𝐺𝑥(𝑦, 𝑥)) (3) HOG is a good performer of human facial detection, however, it is very sensitive to image rotation. Because the HOG shows occurrences of specific gradient orientation, the histogram can be considerably changed by image rotation. Therefore, HOG is not suitable as feature vectors for classification of objects which are often detected as rotated images or texture images, hence the need to augment HOG with Linear SVM.
- 3. References [1] Adrian Rosebrock “Facial landmarks with dlib, OpenCV, and Python,” https://www.pyimagesearch.com/2017/04/03/facial-landmarks-dlib-opencv-python/ [2] Nicolas Delbiaggio, A Comparison of Facial Recognition’s Algorithms. [3] R.Angeline, Kavithvajen.K, Toshita Balaji, Malavika Saji, Sushmitha.S.R, “CNN Integrated With HOG For Efficient Face Recognition”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-6, March 2019. [4] Nadia Jmour; Sehla Zayen; Afef Abdelkrim, “Convolutional Neural networks for image classification”, 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), 22-25 March 2018. [5] Barnabas Takacs and Harry Wechsleri; “Detection Of Faces And Facial Landmarks Using Iconic Filter Banks”, Department of Computer Science, George Mason University, Fairfax, VA 22030, U.S.A