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.
Pose and Illumination in Face Recognition Using Enhanced Gabor LBP & PCA IJMER
This paper presents the face recognition based on Enhanced GABOR LBP and PCA. Some
of the challenges in face recognition are occlusion, pose and illumination .In this paper, we are more
focused on varying pose and illumination. We divided this algorithm into five stages. First stage finds
the fiducial points on face using Gabor filter bank as this filter is well known for illumination
compensation. Second stage applies the morphological techniques for reduce useless fiducial points.
Third stage applies the LBP on reduced fiducial points with neighborhood pixel for improving the pose
variation. Forth stage uses PCA to detect the best variance points which are necessary to characterize
the training images. The last recognition stage includes finding the Euclidean norm of the feature
weight vectors with the test weight vector. In this project, we used 20 images of 20 different persons
from ORL database for training. For testing, we used images with varying illumination, pose and
occluded images of the same training
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This paper presents a new local facial feature descriptor, Local Gray Code Pattern (LGCP), for facial expression recognition in contrast to widely adopted Local Binary pattern. Local Gray Code Pattern (LGCP) characterizes both the texture and contrast information of facial components. The LGCP descriptor is obtained using local gray color intensity differences from a local 3x3 pixels area weighted by their corresponding TF (term frequency). I have used extended Cohn-Kanade expression (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset with a Multiclass Support Vector Machine (LIBSVM) to evaluate proposed method. The proposed method is performed on six and seven basic expression classes in both person dependent and independent environment. According to extensive experimental results with prototypic expressions on static images, proposed method has achieved the highest recognition rate, as compared to other existing appearance-based feature descriptors LPQ, LBP, LBPU2, LBPRI, and LBPRIU2.
Local directional number pattern for face analysis face and expression recogn...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
An improved double coding local binary pattern algorithm for face recognitioneSAT Journals
Abstract A human face conveys a lot of information about the identity and emotional state of the person. So now a day’s face recognition has become an interesting and challenging problem. Face recognition plays a vital role in many applications such as authenticating a person, system security, verification and identification for law enforcement and personal identification among others. So our research work mainly consists of three parts, namely face representation, feature extraction and classification. The first part, Face representation represents how to model a face and check which algorithms can be used for detection and recognition purpose. In the second phase i.e. feature extraction phase we compute the unique features of the face image. In the classification phase the computed DLBP face image is compared with the images from the database. In our research work, we use Double Coding Local Binary Patterns to evaluate face recognition which concentrate over both the shape and texture information to represent face images for person independent face recognition. The face area is firstly cut into small regions from which Local Binary Patterns (LBP), then we compute histograms to generate LBP image then we compute single oriented mean image from which we again compute histogram values small regions and at last concatenated into a single feature vectors and generate D-LBP image. This feature are used for the representation of the face and to measure similarities between images. Keywords: local binary pattern (LBP), double coding local binary pattern (D-LBP), features extraction, classification, pattern recognition, histogram, feature vector.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Pose and Illumination in Face Recognition Using Enhanced Gabor LBP & PCA IJMER
This paper presents the face recognition based on Enhanced GABOR LBP and PCA. Some
of the challenges in face recognition are occlusion, pose and illumination .In this paper, we are more
focused on varying pose and illumination. We divided this algorithm into five stages. First stage finds
the fiducial points on face using Gabor filter bank as this filter is well known for illumination
compensation. Second stage applies the morphological techniques for reduce useless fiducial points.
Third stage applies the LBP on reduced fiducial points with neighborhood pixel for improving the pose
variation. Forth stage uses PCA to detect the best variance points which are necessary to characterize
the training images. The last recognition stage includes finding the Euclidean norm of the feature
weight vectors with the test weight vector. In this project, we used 20 images of 20 different persons
from ORL database for training. For testing, we used images with varying illumination, pose and
occluded images of the same training
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This paper presents a new local facial feature descriptor, Local Gray Code Pattern (LGCP), for facial expression recognition in contrast to widely adopted Local Binary pattern. Local Gray Code Pattern (LGCP) characterizes both the texture and contrast information of facial components. The LGCP descriptor is obtained using local gray color intensity differences from a local 3x3 pixels area weighted by their corresponding TF (term frequency). I have used extended Cohn-Kanade expression (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset with a Multiclass Support Vector Machine (LIBSVM) to evaluate proposed method. The proposed method is performed on six and seven basic expression classes in both person dependent and independent environment. According to extensive experimental results with prototypic expressions on static images, proposed method has achieved the highest recognition rate, as compared to other existing appearance-based feature descriptors LPQ, LBP, LBPU2, LBPRI, and LBPRIU2.
Local directional number pattern for face analysis face and expression recogn...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
An improved double coding local binary pattern algorithm for face recognitioneSAT Journals
Abstract A human face conveys a lot of information about the identity and emotional state of the person. So now a day’s face recognition has become an interesting and challenging problem. Face recognition plays a vital role in many applications such as authenticating a person, system security, verification and identification for law enforcement and personal identification among others. So our research work mainly consists of three parts, namely face representation, feature extraction and classification. The first part, Face representation represents how to model a face and check which algorithms can be used for detection and recognition purpose. In the second phase i.e. feature extraction phase we compute the unique features of the face image. In the classification phase the computed DLBP face image is compared with the images from the database. In our research work, we use Double Coding Local Binary Patterns to evaluate face recognition which concentrate over both the shape and texture information to represent face images for person independent face recognition. The face area is firstly cut into small regions from which Local Binary Patterns (LBP), then we compute histograms to generate LBP image then we compute single oriented mean image from which we again compute histogram values small regions and at last concatenated into a single feature vectors and generate D-LBP image. This feature are used for the representation of the face and to measure similarities between images. Keywords: local binary pattern (LBP), double coding local binary pattern (D-LBP), features extraction, classification, pattern recognition, histogram, feature vector.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hybrid Domain based Face Recognition using DWT, FFT and Compressed CLBPCSCJournals
The characteristics of human body parts and behaviour are measured with biometrics, which are used to authenticate a person. In this paper, we propose Hybrid Domain based Face Recognition using DWT, FFT and Compressed CLBP. The face images are preprocessed to enhance sharpness of images using Discrete Wavelet Transform (DWT) and Laplacian filter. The Compound Local Binary Pattern (CLBP) is applied on sharpened preprocessed face image to compute magnitude and sign components. The histogram is applied on CLBP components to compress number of features. The Fast Fourier Transformation (FFT) is applied on preprocessed image and compute magnitudes. The histogram features and FFT magnitude features are fused to generate final feature. The Euclidian Distance (ED) is used to compare final features of test face images with data base face images to compute performance parameters. It is observed that the percentage recognition rate is high in the case of proposed algorithm compared to existing algorithms.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
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.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
Facial Expression Recognition Using SVM Classifierijeei-iaes
Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels. First, in the bottom level, facial feature tracking, which usually detects and tracks prominent landmarks surrounding facial components (i.e., mouth, eyebrow, etc), captures the detailed face shape information; Second, facial actions recognition, i.e., recognize facial action units (AUs) defined in FACS, try to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize facial expressions that represent the human emotion states. In this proposed algorithm initially detecting eye and mouth, features of eye and mouth are extracted using Gabor filter, (Local Binary Pattern) LBP and PCA is used to reduce the dimensions of the features. Finally SVM is used to classification of expression and facial action units.
J.K.Jeevitha ,B.Karthika,E.Devipriya "Face Recognition using LDN Code", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods. An LDN code is obtained by computing the edge response values in 8 directions at each pixel with the aid of a compass mask. Image analysis and understanding has recently received significant attention, especially during the past several years. At least two reasons can be accounted for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after nearly 30 years of research. In this paper we propose a novel local feature descriptor, called Local Directional Number Pattern (LDN), for face analysis, i.e., face and expression recognition. LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods.
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.
BEB801 Project. Facial expression recognition android application. Student: Alexander Fernicola. Supervisor: Professor Vinod Chandran. Describes the first steps of building an android application that can detect the users facial expression in an image or in video.
Enhanced Face Detection Based on Haar-Like and MB-LBP FeaturesDr. Amarjeet Singh
The effective real-time face detection framework
proposed by Viola and Jones gained much popularity due its
computational efficiency and its simplicity. A notable
variant replaces the original Haar-like features with MBLBP (Multi-Block Local Binary Pattern) which are defined
by the local binary pattern operator, both detector types are
integrated into the OpenCV library. However, each
descriptor and its evaluation method has its own set of
strengths and setbacks. In this paper, an enhanced two-layer
face detector composed of both Haar-like and MB-LBP
features is presented. Haar-like features are employed as a
coarse filter but with a new evaluation involving dual
threshold. The already established MB-LBPs are arranged
as the fine filter of the detector. The Gentle AdaBoost
learning algorithm is deployed for the training of the
proposed detector to reach the classification and
performance potential. Experiments show that in the early
stages of classification, Haar features with dual threshold
are more discriminative than MB-LBP and original Haarlike features with respect to number of features required
and computation. Benchmarking the proposed detector
demonstrate overall 12% higher detection rate at 17% false
alarm over using MB-LBP features singly while performing
with ×3 speedup.
Facial expression identification by using features of salient facial landmarkseSAT Journals
Abstract
Facial expression recognition/identification (FER) systems plays vital role in the field of biometrics. Localizing the facial components accurately is a challenging task in image analysis and computer vision. Accurate detection of face and facial components gives effective performance with classification of expressions. This paper proposes feature based facial recognition system using JAFFE and CK databases. 18 facial landmarks were located using Haar cascade classifier. The distances between 12 points were extracted as features. These features were classified using SVM and K-NN classifier and comparison based on accuracy and execution time is done. The proposed algorithm gives better performance.
Skin colour information and Haar feature based Face DetectionIJERA Editor
In today’s world security of data, person and information is very important aspects.So biometric systems for
user authentication are becoming increasingly popular due to the security control requirement in identity
verification, access control, and surveillance applications. For authentication various recognition techniques are
used e.g. vein pattern recognition, face recognition. For face recognition accurate face detection is primary need.
Here we present two different approaches for face detection. First face detection approach is based on skin
colour detection. Second approach is Haar feature based face detection.
FACIAL EXPRESSION RECOGNITION USING DIGITALISED FACIAL FEATURES BASED ON ACTI...csandit
Facial Expression Recognition is a hot topic in recent years. As artificial intelligent technology is growing rapidly, to communicate with machines, facial expression recognition is essential.The recent feature extraction methods for facial expression recognition are similar to face
recognition, and those caused heavy load for calculation. In this paper, Digitalized Facial Features based on Active Shape Model method is used to reduce the computational complexity
and extract the most useful information from the facial image. The result shows by using this
method the computational complexity is dramatically reduced, and very good performance was obtained compared with other extraction methods.
Feature extraction is becoming popular in face recognition method. Face recognition is the interesting and growing area in real time applications. In last decades many of face recognitions methods has been developed. Feature extraction is the one of the emerging technique in the face recognition methods. In this method an attempt to show best faces recognition method. Here used different descriptors combination like LBP and SIFT, LBP and HOG for feature extraction. Using a single descriptor is difficult to address all variations so combining multiple features in common. Find LBP and SIFT features separately from the images and fuse them with a canonical correlation analysis and same procedure also done using LBP and HOG. The SIFT features have some limitations they don’t work well with lighting changes, quite slow, and mathematically complicated and computationally heavy. The combinations of HOG and LBP features make the system robust against some variations like illumination and expressions. Also, face recognition technique used a different classifier to extract the useful information from images to solve the problems. This paper is organized into four sections. Introduction in the first section. The second section describes feature descriptors and the third section describes proposed methods, final sections describes experiments result and conclusion phase.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hybrid Domain based Face Recognition using DWT, FFT and Compressed CLBPCSCJournals
The characteristics of human body parts and behaviour are measured with biometrics, which are used to authenticate a person. In this paper, we propose Hybrid Domain based Face Recognition using DWT, FFT and Compressed CLBP. The face images are preprocessed to enhance sharpness of images using Discrete Wavelet Transform (DWT) and Laplacian filter. The Compound Local Binary Pattern (CLBP) is applied on sharpened preprocessed face image to compute magnitude and sign components. The histogram is applied on CLBP components to compress number of features. The Fast Fourier Transformation (FFT) is applied on preprocessed image and compute magnitudes. The histogram features and FFT magnitude features are fused to generate final feature. The Euclidian Distance (ED) is used to compare final features of test face images with data base face images to compute performance parameters. It is observed that the percentage recognition rate is high in the case of proposed algorithm compared to existing algorithms.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
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.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
Facial Expression Recognition Using SVM Classifierijeei-iaes
Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels. First, in the bottom level, facial feature tracking, which usually detects and tracks prominent landmarks surrounding facial components (i.e., mouth, eyebrow, etc), captures the detailed face shape information; Second, facial actions recognition, i.e., recognize facial action units (AUs) defined in FACS, try to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize facial expressions that represent the human emotion states. In this proposed algorithm initially detecting eye and mouth, features of eye and mouth are extracted using Gabor filter, (Local Binary Pattern) LBP and PCA is used to reduce the dimensions of the features. Finally SVM is used to classification of expression and facial action units.
J.K.Jeevitha ,B.Karthika,E.Devipriya "Face Recognition using LDN Code", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods. An LDN code is obtained by computing the edge response values in 8 directions at each pixel with the aid of a compass mask. Image analysis and understanding has recently received significant attention, especially during the past several years. At least two reasons can be accounted for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after nearly 30 years of research. In this paper we propose a novel local feature descriptor, called Local Directional Number Pattern (LDN), for face analysis, i.e., face and expression recognition. LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods.
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.
BEB801 Project. Facial expression recognition android application. Student: Alexander Fernicola. Supervisor: Professor Vinod Chandran. Describes the first steps of building an android application that can detect the users facial expression in an image or in video.
Enhanced Face Detection Based on Haar-Like and MB-LBP FeaturesDr. Amarjeet Singh
The effective real-time face detection framework
proposed by Viola and Jones gained much popularity due its
computational efficiency and its simplicity. A notable
variant replaces the original Haar-like features with MBLBP (Multi-Block Local Binary Pattern) which are defined
by the local binary pattern operator, both detector types are
integrated into the OpenCV library. However, each
descriptor and its evaluation method has its own set of
strengths and setbacks. In this paper, an enhanced two-layer
face detector composed of both Haar-like and MB-LBP
features is presented. Haar-like features are employed as a
coarse filter but with a new evaluation involving dual
threshold. The already established MB-LBPs are arranged
as the fine filter of the detector. The Gentle AdaBoost
learning algorithm is deployed for the training of the
proposed detector to reach the classification and
performance potential. Experiments show that in the early
stages of classification, Haar features with dual threshold
are more discriminative than MB-LBP and original Haarlike features with respect to number of features required
and computation. Benchmarking the proposed detector
demonstrate overall 12% higher detection rate at 17% false
alarm over using MB-LBP features singly while performing
with ×3 speedup.
Facial expression identification by using features of salient facial landmarkseSAT Journals
Abstract
Facial expression recognition/identification (FER) systems plays vital role in the field of biometrics. Localizing the facial components accurately is a challenging task in image analysis and computer vision. Accurate detection of face and facial components gives effective performance with classification of expressions. This paper proposes feature based facial recognition system using JAFFE and CK databases. 18 facial landmarks were located using Haar cascade classifier. The distances between 12 points were extracted as features. These features were classified using SVM and K-NN classifier and comparison based on accuracy and execution time is done. The proposed algorithm gives better performance.
Skin colour information and Haar feature based Face DetectionIJERA Editor
In today’s world security of data, person and information is very important aspects.So biometric systems for
user authentication are becoming increasingly popular due to the security control requirement in identity
verification, access control, and surveillance applications. For authentication various recognition techniques are
used e.g. vein pattern recognition, face recognition. For face recognition accurate face detection is primary need.
Here we present two different approaches for face detection. First face detection approach is based on skin
colour detection. Second approach is Haar feature based face detection.
FACIAL EXPRESSION RECOGNITION USING DIGITALISED FACIAL FEATURES BASED ON ACTI...csandit
Facial Expression Recognition is a hot topic in recent years. As artificial intelligent technology is growing rapidly, to communicate with machines, facial expression recognition is essential.The recent feature extraction methods for facial expression recognition are similar to face
recognition, and those caused heavy load for calculation. In this paper, Digitalized Facial Features based on Active Shape Model method is used to reduce the computational complexity
and extract the most useful information from the facial image. The result shows by using this
method the computational complexity is dramatically reduced, and very good performance was obtained compared with other extraction methods.
Feature extraction is becoming popular in face recognition method. Face recognition is the interesting and growing area in real time applications. In last decades many of face recognitions methods has been developed. Feature extraction is the one of the emerging technique in the face recognition methods. In this method an attempt to show best faces recognition method. Here used different descriptors combination like LBP and SIFT, LBP and HOG for feature extraction. Using a single descriptor is difficult to address all variations so combining multiple features in common. Find LBP and SIFT features separately from the images and fuse them with a canonical correlation analysis and same procedure also done using LBP and HOG. The SIFT features have some limitations they don’t work well with lighting changes, quite slow, and mathematically complicated and computationally heavy. The combinations of HOG and LBP features make the system robust against some variations like illumination and expressions. Also, face recognition technique used a different classifier to extract the useful information from images to solve the problems. This paper is organized into four sections. Introduction in the first section. The second section describes feature descriptors and the third section describes proposed methods, final sections describes experiments result and conclusion phase.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Accurate Facial Component Detection Using Gabor FilterjournalBEEI
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
Facial expression identification by using features of salient facial landmarkseSAT Journals
Abstract
Facial expression recognition/identification (FER) systems plays vital role in the field of biometrics. Localizing the facial components accurately is a challenging task in image analysis and computer vision. Accurate detection of face and facial components gives effective performance with classification of expressions. This paper proposes feature based facial recognition system using JAFFE and CK databases. 18 facial landmarks were located using Haar cascade classifier. The distances between 12 points were extracted as features. These features were classified using SVM and K-NN classifier and comparison based on accuracy and execution time is done. The proposed algorithm gives better performance.
K-nearest neighbor based facial emotion recognition using effective featuresIAESIJAI
In this paper, an experiment has been carried out based on a simple k-nearest
neighbor (kNN) classifier to investigate the capabilities of three extracted
facial features for the better recognition of facial emotions. The feature
extraction techniques used are histogram of oriented gradient (HOG), Gabor,
and local binary pattern (LBP). A comparison has been made using
performance indices such as average recognition accuracy, overall
recognition accuracy, precision, recall, kappa coefficient, and computation
time. Two databases, i.e., Cohn-Kanade (CK+) and Japanese female facial
expression (JAFFE) have been used here. Different training to testing data
division ratios is explored to find out the best one from the performance
point of view of the three extracted features, Gabor produced 94.8%, which
is the best among all in terms of average accuracy though the computational
time required is the highest. LBP showed 88.2% average accuracy with a
computational time less than that of Gabor while HOG showed minimum
average accuracy of 55.2% with the lowest computation time.
Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Lo...IJECEIAES
The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: firstly, both Gabor filters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database.
Multi Local Feature Selection Using Genetic Algorithm For Face IdentificationCSCJournals
Face recognition is a biometric authentication method that has become more significant and relevant in recent years. It is becoming a more mature technology that has been employed in many large scale systems such as Visa Information System, surveillance access control and multimedia search engine. Generally, there are three categories of approaches for recognition, namely global facial feature, local facial feature and hybrid feature. Although the global facial-based feature approach is the most researched area, this approach is still plagued with many difficulties and drawbacks due to factors such as face orientation, illumination, and the presence of foreign objects. This paper presents an improved offline face recognition algorithm based on a multi-local feature selection approach for grayscale images. The approach taken in this work consists of five stages, namely face detection, facial feature (eyes, nose and mouth) extraction, moment generation, facial feature classification and face identification. Subsequently, these stages were applied to 3065 images from three distinct facial databases, namely ORL, Yale and AR. The experimental results obtained have shown that recognition rates of more than 89% have been achieved as compared to other global-based features and local facial-based feature approaches. The results also revealed that the technique is robust and invariant to translation, orientation, and scaling.
Facial Expression Recognition System Based on SVM and HOG TechniquesCSCJournals
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 Spectral Domain Local Feature Extraction Algorithm for Face RecognitionCSCJournals
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.
Abstract: Face Recognition appears to be an integral part in human-computer interfaces and eservices. To carry out security and fault tolerance various Image Processing techniques have been incorporated using ‘Curse of Dimensionality’ that refers to Classifying a pattern with high dimensions that requires a large number of training data. A face recognition & Detection system is a computer-driven application for automatically identifying or verifying a person from still or video image. It does that by comparing selected facial features in the live image and a facial database where the system returns a possible list of faces corresponding to training samples from the database. The nodal points are measured creating a numerical code, called a faceprint, representing the face in the database. Relatively many techniques are used. Image processing technique has been implemented using Feature extraction by Gabor Filters and database training data using Neural Networks. Multiscale resolution and matrix sampling is efficiently performed using this technique.
Keywords: Image Processing techniques, Curse of Dimensionality, Faceprint, Feature extraction, Gabor Filters, Neural Networks.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
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1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
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DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
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👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Search and Society: Reimagining Information Access for Radical Futures
A04430105
1. IOSR Journal of Engineering (IOSRJEN) www.iosrjen.org
ISSN (e): 2250-3021, ISSN (p): 2278-8719
Vol. 04, Issue 04 (April. 2014), ||V3|| PP 01-05
International organization of Scientific Research 1 | P a g e
A Survey of Facial Expression Recognition Methods
Shail Kumari Shah
Department of CSE, Rajasthan College of Engineering for Women, Jaipur, India
Abstract: - Facial expression recognition (anger, sad, happy, disgust, surprise, fear expressions) is application
of advanced object detection, pattern recognition and classification task. Facial expression is one of the most
powerful and natural means for human beings to show their emotions. It has found its applications in human-
computer interaction (HCI), robotics, border security systems, forensics, machine vision, video conferencing,
user profiling for customer satisfaction, physiological research etc. Although humans can detect facial
expressions with less effort and delay but it is still a challenge for the machine to fast and effectively detect
facial expressions. Therefore algorithms should be developed to thought machines to understand facial gestures.
This paper focuses on a review of different techniques for facial expression recognition.
Keywords: - Gabor filter, Log-Gabor filter, Local Binary Pattern, PCA, LDA, SVM, K-NN
I. INTRODUCTION
Facial expression is one of the most Facial expression is one of the most powerful and natural means
for human beings to show their emotions. It is the position or movement of the muscles beneath the skin of the
face. This movements show the emotional state of an individual. The research study by Mehrabian [11] has
indicated that 7% of the communication information is transferred by linguistic language, 38% by paralanguage,
and 55% by facial expressions in human face-to-face communication. This, therefore, shows that facial
expressions provide a large amount of information in human communication.
Recognition of facial expression is often a hard task. FACS (Facial Action Coding Systems) describes
the changes in facial expression that human can detect by observing changes in facial muscles. Each component
of facial movement is called an Action Units. It was published by Ekman and Friesen in 1978 [1]. FACS
describes 44 Action Units.
Facial expression recognition is an interesting and challenging area. Its application is found in many areas like
human-computer interaction (HCI), robotics(AIBO robots), border security systems, forensics, machine vision,
video conferencing, user profiling for customer satisfaction, physiological research etc.
Facial expression analysis consists of two different approaches and each approach has two different
methodologies. When whole of the frontal face is use and processes in order to end up with the classifications of
6 universal facial expression prototypes: disgust, fear, joy, surprise, sadness and anger gives the outlines the first
approach. Instead of using the face images as a whole, dividing them into some sub-sections for further
processing forms up the main idea of the second approach for facial expression recognition. Geometric Based
Parameterization is an old way which consists of tracking and processing the motions of some spots on image
sequences. Facial motion parameters and the tracked spatial positioning & shapes of some special points on face,
are used as feature vectors for the geometric based method. Rather than tracking spatial points and using
positioning and movement parameters that vary within time, colour (pixel) information of related regions of face
are processed in Appearance Based Parameterizations.
In this paper we provide a critical review of the most recent development in facial expression recognition. This
paper is organized as follows: In Section II pre-processing task is explained. Section III provides a detailed
review of some facial expression recognition methodologies. Finally, conclusions are in Section IV.
II. IMAGE PRE-PROCESSING
Noise removal, normalisation against the variation of pixel position or brightness, segmentation,
location or tracking of the face or its part comes under pre-processing phase. Rotation of the head and changes
in illumination are one of the most important factors affecting the performance of facial expression recognition.
Illumination affect can be removed by using Gabor filter. Turning of head can be eliminated by choosing central
points of the eyes manually. Then the images are turned up to a point that the X parameter gets the same
dimensions as the central points of the eye [12]. Then the face is cropped in rectangular according to face model
explained in [8]. A variety of face detection techniques exists like Viola-Jones Method, Exhaustive search,
Branch and Bound. However robust detection of face is still a difficult task.
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International organization of Scientific Research 2 | P a g e
III. METHODOLOGY
A. Feature Extraction
Feature extraction is one of the Feature extraction is one of the most imporatnt part of facial expression
recognition. It is the process of extracting and isolating imporatant desired feature from the face. Feature
extraction converts pixel data into a higher-level representation - of shape, motion, colours, texture, and spatial
configuration of the face or its components [fer-brief tutorial]. Deriving an effective facial representation from
original images is an important step for successful facial expression recognition. If inadequate features are used,
even the best classifier fails to achieve accurate recognition. In this paper we will discuss Gabor filter, Log-
Gabor filter, Local Binary Pattern for feature extraction.
1) Gabor filter: For feature extraction Gabor filters are used. Gabor filters are applied to image to extract
features aligned at certain angle and frequencies. It has advantage of having optimal localisation in both
frequency and spatial domain. Certain orientations and frequencies are selected and used to differentiate
between different facial expressions in images. A Gabor filter can be represented by the following equation:
g(x, y) = s(x, y)w(x, y)
Where s(x, y) is a complex sinusoidal known as the carrier, and w(x, y) is a 2-D Gaussian-shaped function
known as the envelope [10].
Fig.1 The real part of the Gabor Filters
Fig.2 Sample image
Fig.3 The magnitudes of the Gabor feature representation of the sample face image
There are various forms to define this function, one normalized 2-D form being:
Where
x’ = x cos𝜃 + y sin𝜃
y’ = -x sin𝜃 + ycos𝜃
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In this equation (x, y) is the pixel position in spatial domain, represents the wavelength (reciprocal of
frequency) in pixels of sinusoidal plane wave, 𝜃 represents the orientation of the Gabor filter, is the phase
offset, 𝜎 is the spatial width of the Gaussian envelope and 𝛾 is the spatial aspect ratio, and specifies the
ellipticity of the support of the Gabor function. In most of the cases Gabor filter bank with 5 frequencies and 8
orientations is used to extract the Gabor features for face representation.
2) Log-Gabor Filter: Gabor filters are a traditional choice for obtaining localized frequency information.
They offer the best simultaneous localization of spatial and frequency information. However they have two
main limitations. The maximum bandwidth of a Gabor filter is limited to approximately one octave and Gabor
filters are not optimal if one is seeking broad spectral information with maximal spatial localization. An
alternative to the Gabor function is the Log-Gabor function proposed by Field [1987]. Log-Gabor filters can be
constructed with arbitrary bandwidth and the bandwidth can be optimized to produce a filter with minimal
spatial extent. Gabor filters are not optimal to achieve broad spectral information with the maximum spatial
localization. Furthermore, the Gabor filters are band pass filters, which may suffer from lost of the low and the
high-frequency information. To achieve the broad spectral information and to overcome the bandwidth
limitation of the traditional Gabor filter, Field proposed Log-Gabor filter. The response of the Log-Gabor filter
is Gaussian when viewed on a logarithmic frequency scale instead of a linear one. This allows more information
to be captured in the high-frequency areas with desirable high pass characteristics. One cannot construct Gabor
functions of arbitrarily wide bandwidth and still maintain a reasonably small DC component in the even-
symmetric filter. Log-Gabor functions, by definition, always have no DC component.
𝐺(𝑓) = 𝑒𝑥𝑝
− 𝑙𝑜𝑔(
𝑓
𝑓0
)
2
2 𝑙𝑜𝑔(
𝜎
𝑓0
)
2
Therefore Log-Gabor filter can achieve better performance than Gabor filter.
3) Local Binary pattern: The original LBP operator was introduced by Ojala et al. [7] and was proved a
powerful means of texture description. LBP operator takes the signs of the pixel differences between a pixel and
its neighboring pixels to a binary code called LBP codes. Then the histogram of the binary code of image block
is used for further analysis.
The first experiment of the LBP operator worked with eight neighbors i.e. 3×3 neighborhood of each
pixel with the center value as a threshold. Where the center pixel’s value is greater than the neighbor’s value, we
write “1”. Otherwise, write “0”. This gives 8 bit binary number. This binary number is then converted into
decimal one. Based on the operator, each pixel of an image is labeled with an LBP code. Each code is called
pattern. In this way each pixel is numbered.
Pattern: 11101010
LBP code: 234
Fig. 4 An example of basic LBP operator
The limitation of the basic LBP operator is its small 3 ×3 neighborhood which cannot capture dominant
features with large scale structures. Hence the operator later was extended to use neighborhood of different
sizes. Using circular neighborhoods and bilinear interpolating the pixel values allows any radius and number of
pixels in the neighborhood. In extended LBP operator , notation (P,R) is used to denotes neighborhood of P
equally spaced sampling points on circle of radius R. LBP is robust against illumination variations because it
only depends on magnitude relation between center pixel and its surrounding pixels.
B. Feature Reduction
Feature reduction or feature compression is a process of reducing excessive dimensionality of extracted
feature from feature extraction step. Linear methods project the high dimensional data onto a lower dimensional
space. There are two classical methods called PCA (Principle Component Analysis) and LDA (Linear
Discriminent Analysis).
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1) Principle Component Analysis (PCA): Principle Component Analysis also known as Karhunen-Loeve
method is one of the most popular method for dimension reduction. It is also known as Eigenface method. PCA
is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate
system such that the greatest variance by any projection of the data come to lie on the first coordinate (called the
first principle component), the second greatest variance on the second coordinate and so on. [13].
Consider a set of N sample images, {X1, X2,....XN} represented by t-dimensional Gabor feature vector. The PCA
[5] [4] can be used to find a linear transformation mapping the original t-dimensional feature space into a g-
dimensional feature subspace, where normally g<<t, the new feature vector is conducted with following
equation:
𝛾𝑖 = 𝑊𝑝 𝑋𝑖 i =1... N
Where Wp is the linear transformations matrix, i is the number of sample images.
The columns of Wp are the g eigenvectors associated with the g largest eigenvalues of the scatter matrix ST,
which is defined as
𝑆 𝑇 = (𝑋𝑖 − 𝜇)(𝑋𝑖 − 𝜇) 𝑇
𝑁
𝑖=1
𝜇 =
1
𝑁
𝑋𝑖
𝑁
𝑖=1
2) Linear Discriminant Analysis (LDA): LDA is a powerful method for face recognition. It linearly transforms
the original data space into low-dimensional feature space where data is well separated. The main difference
between PCA and LDA is that in PCA the shape and location of the original data changes after transform while
LDA doesn’t change the location but tries to provide more class separability by drawing a decision region
between the classes.
Let suppose there are C classes.
Let µi be the mean vector of class i, i =1,2,3….,C
Let Mi be the number of samples within class i, i=1,2,…C
Let M= be the total number of samples.
In LDA, within class and between class scatter are used to formulate criteria for class separability. Within class
scatter is the expected covariance of each of the classes. The scatter measures are computed using the equation:
Between classes scatter matrix can be calculated using equation:
Where µ is the mean of the entire dataset.
LDA computes transformation that maximizes the between class scatter while minimize the within class scatter.
Maximize=det (Sb)/det (Sw).
The solution obtained by maximizing this criterion defines the axes of the transformed space. The linear
transformation is given by a matrix whose columns are the eigenvector of
.
IV. CLASSIFICATION
Different expressions are categorised by a classifier. The two main types of classes used in facial
expression recognition are action units (AUs) [3], and the prototypic facial expressions defined by Ekman [2].
The 6 prototypic expressions are happiness, sadness, anger, surprise, fear, and disgust. An AU is one of the 46
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International organization of Scientific Research 5 | P a g e
atomic elements of visible facial movements or its associated deformation. These AUs are described in Facial
Action Coding System (FACS).
1) Support Vector Machine (SVM): SVM are based on the concept of decision planes that define decision
boundaries [6]. In this approach optimal classification of a separable two class problem is achieved by
maximizing the width of the empty area (margin) between two classes. The margin width is defined as the
distance between the discrimination hyper surface in n-dimensional feature space and the closest training pattern.
The output of the SVM system is a label that classifies the grid under examination to one of the two basic
(neutral, happy) facial expressions. One of the disadvantages of SVM is that it cannot be applied when the
feature vectors defining samples have missing entries. However advantage of SVM over other classifiers is that
SVM can achieve better generalization performance.
2) K-Nearest Neighbours (K-NN): K-NN is one of the simplest machine learning algorithms used for
classification. The K-Nearest Neighbours (K-NN) method is a classical classification algorithm where the input
feature vector is classified based on the class represented by the majority of the K nearest feature vectors
obtained during the training process. The training examples are vectors in a multidimensional feature space,
each with a class label. The training phase of the algorithm consists only of storing the feature vectors and class
labels of the training samples. In the classification phase, k is a user-defined constant, and an unlabeled vector (a
query or test point) is classified by assigning the label which is most frequent among the k training samples
nearest to that query point. A commonly used distance metric for continuous variables is Euclidean distance. For
discrete variables, such as for text classification, another metric can be used, such as the Hamming distance [14].
V. CONCLUSIONS
This paper has attempted to review a significant number of papers to cover the recent development in
the field of facial expression recognition. Generally speaking, facial expression recognition is a difficult task.
Present study reveals that for better facial expression recognition new algorithms can be develop like using Log
Gabor filter (feature extractor) , PCA+LDA (feature reduction) and ANN may yield better result in terms of
performance and accuracy.
REFERENCES
[1] P. Ekman and W.V. Friesen, Facial Action Coding System, Palo Alto: Consulting Psychologist Press,
1978.
[2] P. Ekman, Emotion in the Human Face, Cambridge University Press, 1982.
[3] G. Donato, M.S. Bartlett, J.C. Hager, P.Ekman, T.J.Sejnowski, “Classifying Facial Action”, IEEE Trans.
Pattern Analysis and Machine Intelligence, Vol. 21, No.10, pp.974-989,1999.
[4] R. O. Duda, P. E. Hart, D. G. Stork, “Pattern Classification.” Wiley, New York (2001).
[5] A. M. Martinez, A. C. Kak, “PCA versus LDA”, IEEE Trans. Pattern Analysis and Machine Intelligence,
Vol. 23, 2001, pp.228-233.
[6] C.W. Hsu and C. J. Lin, “A comparison of Methods for multiclass Support Vector Machines”, IEEE
Transactions on Neural Networks, vol. 13, no. 2, pp.415-425, 2002.
[7] T. Ojala, M. Pietikainen, T. Maenpaa, “Multiresolution gray scale and rotation invariant texture
classification with local binary pattern”, IEEE Transaction on Pattern Analysis and Machine Intelligence,
ISSN 971–987, 2002.
[8] F.Y. Shih, C. Chaung, “Automatic extraction of head and face boundaries and facial feature”,
Information Sciences, Vol. 158, 2004, pp.117-130.
[9] Hong-Bo Deng, Lian-Wen Jin, Li-Xin Zhen, Jian-Cheng Huang, “A New Facial Expression Recognition
Method Based on Local Gabor Filter Bank and PCA plus LDA”, International Journal of Information
Technology, Vol. 11, No. 11 ,2005.
[10] Nectarios Rose”, Facial Expression Classification using Gabor and Log-Gabor Filters”, IEEE Computer
Society, 0-7695-2503-2/06, 2006.[
[11] A. Mehrabian, Nonverbal Communication. London, U.K.: Aldine, 2007.
[12] Saeid Fazli, Reza Afrouzian, Hadi Seyedarabi, “ High Performance facial Expression Recognition using
Gabor Filter and Probabilistic Neural Network”, 978-1-4244-4738-1/09, 2009 IEEE.
[13] http://en.wikipedia.org/wiki/Principle_components_analysis.
[14] http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm.