In this paper we address the problem of face recognition using edge information as independent components. The edge information is obtained by using Laplacian of Gaussian (LoG) and Canny edge detection methods then preprocessing is done by using Principle Component analysis (PCA) before applying the Independent Component Analysis (ICA) algorithm for training of images. The independent components obtained by ICA algorithm are used as feature vectors for classification. The Euclidean distance and Mahalanobis distance classifiers are used for testing of images. The algorithm is tested on two different databases of face images for variation in illumination and facial poses up to 180 degree rotation angle.
Facial expression recognition based on wapa and oepa fasticaijaia
Face is one of the most important biometric traits
for its uniqueness and robustness. For this reason
researchers from many diversified fields, like: sec
urity, psychology, image processing, and computer
vision, started to do research on face detection as
well as facial expression recognition. Subspace le
arning
methods work very good for recognizing same facial
features. Among subspace learning techniques PCA,
ICA, NMF are the most prominent topics. In this wor
k, our main focus is on Independent Component
Analysis (ICA). Among several architectures of ICA,
we used here FastICA and LS-ICA algorithm. We
applied Fast-ICA on whole faces and on different fa
cial parts to analyze the influence of different pa
rts for
basic facial expressions. Our extended algorithm WA
PA-FastICA and OEPA-FastICA are discussed in
proposed algorithm section. Locally Salient ICA is
implemented on whole face by using 8x8 windows to
find the more prominent facial features for facial
expression. The experiment shows our proposed OEPA-
FastICA and WAPA-FastICA outperform the existing pr
evalent Whole-FastICA and LS-ICA methods
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
WCTFR : W RAPPING C URVELET T RANSFORM B ASED F ACE R ECOGNITIONcsandit
The recognition of a person based on biological fea
tures are efficient compared with traditional
knowledge based recognition system. In this paper w
e propose Wrapping Curvelet Transform
based Face Recognition (WCTFR). The Wrapping Curve
let Transform (WCT) is applied on
face images of database and test images to derive c
oefficients. The obtained coefficient matrix is
rearranged to form WCT features of each image. The
test image WCT features are compared
with database images using Euclidean Distance (ED)
to compute Equal Error Rate (EER) and
True Success Rate (TSR). The proposed algorithm wit
h WCT performs better than Curvelet
Transform algorithms used in [1], [10] and [11].
An Efficient Face Recognition Using Multi-Kernel Based Scale Invariant Featur...CSCJournals
Face recognition has gained significant attention in research community due to its wide range of commercial and law enforcement applications. Due to the developments in the past few decades, in the current scenario, face recognition is employing advanced feature identification techniques and matching methods. In spite of vast research done, face recognition still remains an open problem due to the challenges posed by illumination, occlusions, pose variation, scaling, etc. This paper is aimed at proposing a face recognition technique with high accuracy. It focuses on face recognition based on improved SIFT algorithm. In the proposed approach, the face features are extracted using a novel multi-kernel function (MKF) based SIFT technique. The classification is done using SVM classifier. Experimental results shows the superiority of the proposed algorithm over the SIFT technique. Evaluation of the proposed approach is done on CVL face database and experimental results shows that the proposed approach has a recognition rate of 99%.
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.
Facial expression recognition based on wapa and oepa fasticaijaia
Face is one of the most important biometric traits
for its uniqueness and robustness. For this reason
researchers from many diversified fields, like: sec
urity, psychology, image processing, and computer
vision, started to do research on face detection as
well as facial expression recognition. Subspace le
arning
methods work very good for recognizing same facial
features. Among subspace learning techniques PCA,
ICA, NMF are the most prominent topics. In this wor
k, our main focus is on Independent Component
Analysis (ICA). Among several architectures of ICA,
we used here FastICA and LS-ICA algorithm. We
applied Fast-ICA on whole faces and on different fa
cial parts to analyze the influence of different pa
rts for
basic facial expressions. Our extended algorithm WA
PA-FastICA and OEPA-FastICA are discussed in
proposed algorithm section. Locally Salient ICA is
implemented on whole face by using 8x8 windows to
find the more prominent facial features for facial
expression. The experiment shows our proposed OEPA-
FastICA and WAPA-FastICA outperform the existing pr
evalent Whole-FastICA and LS-ICA methods
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
WCTFR : W RAPPING C URVELET T RANSFORM B ASED F ACE R ECOGNITIONcsandit
The recognition of a person based on biological fea
tures are efficient compared with traditional
knowledge based recognition system. In this paper w
e propose Wrapping Curvelet Transform
based Face Recognition (WCTFR). The Wrapping Curve
let Transform (WCT) is applied on
face images of database and test images to derive c
oefficients. The obtained coefficient matrix is
rearranged to form WCT features of each image. The
test image WCT features are compared
with database images using Euclidean Distance (ED)
to compute Equal Error Rate (EER) and
True Success Rate (TSR). The proposed algorithm wit
h WCT performs better than Curvelet
Transform algorithms used in [1], [10] and [11].
An Efficient Face Recognition Using Multi-Kernel Based Scale Invariant Featur...CSCJournals
Face recognition has gained significant attention in research community due to its wide range of commercial and law enforcement applications. Due to the developments in the past few decades, in the current scenario, face recognition is employing advanced feature identification techniques and matching methods. In spite of vast research done, face recognition still remains an open problem due to the challenges posed by illumination, occlusions, pose variation, scaling, etc. This paper is aimed at proposing a face recognition technique with high accuracy. It focuses on face recognition based on improved SIFT algorithm. In the proposed approach, the face features are extracted using a novel multi-kernel function (MKF) based SIFT technique. The classification is done using SVM classifier. Experimental results shows the superiority of the proposed algorithm over the SIFT technique. Evaluation of the proposed approach is done on CVL face database and experimental results shows that the proposed approach has a recognition rate of 99%.
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.
Face Recognition Based Intelligent Door Control Systemijtsrd
This paper presents the intelligent door control system based on face detection and recognition. This system can avoid the need to control by persons with the use of keys, security cards, password or pattern to open the door. The main objective is to develop a simple and fast recognition system for personal identification and face recognition to provide the security system. Face is a complex multidimensional structure and needs good computing techniques for recognition. The system is composed of two main parts face recognition and automatic door access control. It needs to detect the face before recognizing the face of the person. In face detection step, Viola Jones face detection algorithm is applied to detect the human face. Face recognition is implemented by using the Principal Component Analysis PCA and Neural Network. Image processing toolbox which is in MATLAB 2013a is used for the recognition process in this research. The PIC microcontroller is used to automatic door access control system by programming MikroC language. The door is opened automatically for the known person according to the result of verification in the MATLAB. On the other hand, the door remains closed for the unknown person. San San Naing | Thiri Oo Kywe | Ni Ni San Hlaing ""Face Recognition Based Intelligent Door Control System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23893.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23893/face-recognition-based-intelligent-door-control-system/san-san-naing
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.
Happiness Expression Recognition at Different Age ConditionsEditor IJMTER
Recognition of different internal emotions of human face under various critical
conditions is a difficult task. Facial expression recognition with different age variations is
considered in this study. This paper emphasizes on recognition of facial expression like
happiness mood of nine persons using subspace methods. This paper mainly focuses on new
robust subspace method which is based on Proposed Euclidean Distance Score Level Fusion
(PEDSLF) using PCA, ICA, SVD methods. All these methods and new robust method is
tested with FGNET database. An automatic recognition of facial expressions is being carried
out. Comparative analysis results surpluses PEDSLF method is more accurate for happiness
emotional facial expression recognition.
Extracted features based multi-class classification of orthodontic images IJECEIAES
The purpose of this study is to investigate computer vision and machine learning methods for classification of orthodontic images in order to provide orthodontists with a solution for multi-class classification of patients’ images to evaluate the evolution of their treatment. Of which, we proposed three algorithms based on extracted features, such as facial features and skin colour using YCbCrcolour space, assigned to nodes of a decision tree to classify orthodontic images: an algorithm for intra-oral images, an algorithm for mould images and an algorithm for extra-oral images. Then, we compared our method by implementing the Local Binary Pattern (LBP) algorithm to extract textural features from images. After that, we applied the principal component analysis (PCA) algorithm to optimize the redundant parameters in order to classify LBP features with six classifiers; Quadratic Support Vector Machine (SVM), Cubic SVM, Radial Basis Function SVM, Cosine K-Nearest Neighbours (KNN), Euclidian KNN, and Linear Discriminant Analysis (LDA). The presented algorithms have been evaluated on a dataset of images of 98 different patients, and experimental results demonstrate the good performances of our proposed method with a high accuracy compared with machine learning algorithms. Where LDA classifier achieves an accuracy of 84.5%.
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.
OPTIMAL CHOICE: NEW MACHINE LEARNING PROBLEM AND ITS SOLUTIONijcsity
We introduce the task of learning to pick a single preferred example out a finite set of examples, an
“optimal choice problem”, as a supervised machine learning problem with complex input. Problems of
optimal choice emerge often in various practical applications. We formalize the problem, show that it does
not satisfy the assumptions of statistical learning theory, yet it can be solved efficiently in some cases. We propose two approaches to solve the problem. Both of them reach good solutions on real life data from a signal processing application.
Statistical Models for Face Recognition System With Different Distance MeasuresCSCJournals
Face recognition is one of the challenging applications of image processing. Robust face recognition algorithm should posses the ability to recognize identity despite many variations in pose, lighting and appearance. Principle Component Analysis (PCA) method has a wide application in the field of image processing for dimension reduction of the data. But these algorithms have certain limitations like poor discriminatory power and ability to handle large computational load. This paper proposes a face recognition techniques based on PCA with Gabor wavelets in the preprocessing stage and statistical modeling methods like LDA and ICA for feature extraction. The classification for the proposed system is done using various distance measure methods like Euclidean Distance(ED), Cosine Distance (CD), Mahalanobis Distance (MHD) methods and the recognition rate were compared for different distance measures. The proposed method has been successfully tested on ORL face data base with 400 frontal images corresponding to 40 different subjects which are acquired under variable illumination and facial expressions. It is observed from the results that use of PCA with Gabor filters and features extracted through ICA method gives a recognition rate of about 98% when classified using Mahalanobis distance classifier. This recognition rate stands better than the conventional PCA and PCA + LDA methods employing other and classifier techniques.
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITIONijsc
The word biometrics refers to the use of physiological or biological characteristics of human to recognize
and verify the identity of an individual. Face is one of the human biometrics for passive identification with
uniqueness and stability. In this manuscript we present a new face based biometric system based on neural
networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the
proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed
method to a variety of datasets and show the results.
Identifying Gender from Facial Parts Using Support Vector Machine ClassifierEditor IJCATR
Gender classification can be stated as inferring female or male from a collection of facial images. There exist different
methods for gender classification, such as gait, iris, hand shape and hair, it is probably better way to find out gender based on facial
features. In this paper SVM basic kernel function has been employed firstly to detect and classify the human gender Image into
two labels i.e. (1) male and (2) female. The gender classifier achieves over 96% accuracy.
Region based elimination of noise pixels towards optimized classifier models ...IJERA Editor
The extraction of the skin pixels in a human image and rejection of non-skin pixels is called the skin segmentation. Skin pixel detection is the process of extracting the skin pixels in a human image which is typically used as a pre-processing step to extract the face regions from human image. In past, there are several computer vision approaches and techniques have been developed for skin pixel detection. In the process of skin detection, given pixels are been transformed into an appropriate color space such as RGB, HSV etc. And then skin classifier model have been applied to label the pixel into skin or non-skin regions. Here in this research a “Region based elimination of noise pixels and performance analysis of classifier models for skin pixel detection applied on human images” would be performed which involve the process of image representation in color models, elimination of non-skin pixels in the image, and then pre-processing and cleansing of the collected data, feature selection of the human image and then building the model for classifier. In this research and implementation of skin pixels classifier models are proposed with their comparative performance analysis. The definition of the feature vector is simply the selection of skin pixels from the human image or stack of human images. The performance is evaluated by comparing and analysing skin colour segmentation algorithms. During the course of research implementation, efforts are iterative which help in selection of optimized skin classifier based on the machine learning algorithms and their performance analysis.
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVMsipij
The biometric is used to identify a person effectively and employ in almost all applications of day to day
activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform
(DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person
into one image using averaging technique is introduced to reduce execution time and memory. The DWT is
applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients
are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
Face Recognition Based Intelligent Door Control Systemijtsrd
This paper presents the intelligent door control system based on face detection and recognition. This system can avoid the need to control by persons with the use of keys, security cards, password or pattern to open the door. The main objective is to develop a simple and fast recognition system for personal identification and face recognition to provide the security system. Face is a complex multidimensional structure and needs good computing techniques for recognition. The system is composed of two main parts face recognition and automatic door access control. It needs to detect the face before recognizing the face of the person. In face detection step, Viola Jones face detection algorithm is applied to detect the human face. Face recognition is implemented by using the Principal Component Analysis PCA and Neural Network. Image processing toolbox which is in MATLAB 2013a is used for the recognition process in this research. The PIC microcontroller is used to automatic door access control system by programming MikroC language. The door is opened automatically for the known person according to the result of verification in the MATLAB. On the other hand, the door remains closed for the unknown person. San San Naing | Thiri Oo Kywe | Ni Ni San Hlaing ""Face Recognition Based Intelligent Door Control System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23893.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23893/face-recognition-based-intelligent-door-control-system/san-san-naing
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.
Happiness Expression Recognition at Different Age ConditionsEditor IJMTER
Recognition of different internal emotions of human face under various critical
conditions is a difficult task. Facial expression recognition with different age variations is
considered in this study. This paper emphasizes on recognition of facial expression like
happiness mood of nine persons using subspace methods. This paper mainly focuses on new
robust subspace method which is based on Proposed Euclidean Distance Score Level Fusion
(PEDSLF) using PCA, ICA, SVD methods. All these methods and new robust method is
tested with FGNET database. An automatic recognition of facial expressions is being carried
out. Comparative analysis results surpluses PEDSLF method is more accurate for happiness
emotional facial expression recognition.
Extracted features based multi-class classification of orthodontic images IJECEIAES
The purpose of this study is to investigate computer vision and machine learning methods for classification of orthodontic images in order to provide orthodontists with a solution for multi-class classification of patients’ images to evaluate the evolution of their treatment. Of which, we proposed three algorithms based on extracted features, such as facial features and skin colour using YCbCrcolour space, assigned to nodes of a decision tree to classify orthodontic images: an algorithm for intra-oral images, an algorithm for mould images and an algorithm for extra-oral images. Then, we compared our method by implementing the Local Binary Pattern (LBP) algorithm to extract textural features from images. After that, we applied the principal component analysis (PCA) algorithm to optimize the redundant parameters in order to classify LBP features with six classifiers; Quadratic Support Vector Machine (SVM), Cubic SVM, Radial Basis Function SVM, Cosine K-Nearest Neighbours (KNN), Euclidian KNN, and Linear Discriminant Analysis (LDA). The presented algorithms have been evaluated on a dataset of images of 98 different patients, and experimental results demonstrate the good performances of our proposed method with a high accuracy compared with machine learning algorithms. Where LDA classifier achieves an accuracy of 84.5%.
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.
OPTIMAL CHOICE: NEW MACHINE LEARNING PROBLEM AND ITS SOLUTIONijcsity
We introduce the task of learning to pick a single preferred example out a finite set of examples, an
“optimal choice problem”, as a supervised machine learning problem with complex input. Problems of
optimal choice emerge often in various practical applications. We formalize the problem, show that it does
not satisfy the assumptions of statistical learning theory, yet it can be solved efficiently in some cases. We propose two approaches to solve the problem. Both of them reach good solutions on real life data from a signal processing application.
Statistical Models for Face Recognition System With Different Distance MeasuresCSCJournals
Face recognition is one of the challenging applications of image processing. Robust face recognition algorithm should posses the ability to recognize identity despite many variations in pose, lighting and appearance. Principle Component Analysis (PCA) method has a wide application in the field of image processing for dimension reduction of the data. But these algorithms have certain limitations like poor discriminatory power and ability to handle large computational load. This paper proposes a face recognition techniques based on PCA with Gabor wavelets in the preprocessing stage and statistical modeling methods like LDA and ICA for feature extraction. The classification for the proposed system is done using various distance measure methods like Euclidean Distance(ED), Cosine Distance (CD), Mahalanobis Distance (MHD) methods and the recognition rate were compared for different distance measures. The proposed method has been successfully tested on ORL face data base with 400 frontal images corresponding to 40 different subjects which are acquired under variable illumination and facial expressions. It is observed from the results that use of PCA with Gabor filters and features extracted through ICA method gives a recognition rate of about 98% when classified using Mahalanobis distance classifier. This recognition rate stands better than the conventional PCA and PCA + LDA methods employing other and classifier techniques.
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
MULTIPLE CONFIGURATIONS FOR PUNCTURING ROBOT POSITIONINGJaresJournal
The paper presents the Inverse Kinematics (IK) close form derivation steps using combination of analytical
and geometric techniques for the UR robot. The innovative application of this work is used in the precise
positioning of puncture robotics system. The end effector is a puncture needle guide tube, which needs
precise positioning over the puncture insertion point. The IK closed form solutions bring out maximum 8
solutions represents 8 different robot joints configurations. These multiple solutions are helpful in the
puncture robotics system, it allow doctors to choose the most suitable configuration during the operation.
Therefore the workspace becomes more adequate for the coexistence of human and robot. Moreover IK
closed form solutions are more precise in positioning for medical puncture surgery compared to other
numerical methods. We include a performance evaluation for both of the IK obtained by the closed form
solution and by a numerical method.
NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITIONijsc
The word biometrics refers to the use of physiological or biological characteristics of human to recognize
and verify the identity of an individual. Face is one of the human biometrics for passive identification with
uniqueness and stability. In this manuscript we present a new face based biometric system based on neural
networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the
proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed
method to a variety of datasets and show the results.
Identifying Gender from Facial Parts Using Support Vector Machine ClassifierEditor IJCATR
Gender classification can be stated as inferring female or male from a collection of facial images. There exist different
methods for gender classification, such as gait, iris, hand shape and hair, it is probably better way to find out gender based on facial
features. In this paper SVM basic kernel function has been employed firstly to detect and classify the human gender Image into
two labels i.e. (1) male and (2) female. The gender classifier achieves over 96% accuracy.
Region based elimination of noise pixels towards optimized classifier models ...IJERA Editor
The extraction of the skin pixels in a human image and rejection of non-skin pixels is called the skin segmentation. Skin pixel detection is the process of extracting the skin pixels in a human image which is typically used as a pre-processing step to extract the face regions from human image. In past, there are several computer vision approaches and techniques have been developed for skin pixel detection. In the process of skin detection, given pixels are been transformed into an appropriate color space such as RGB, HSV etc. And then skin classifier model have been applied to label the pixel into skin or non-skin regions. Here in this research a “Region based elimination of noise pixels and performance analysis of classifier models for skin pixel detection applied on human images” would be performed which involve the process of image representation in color models, elimination of non-skin pixels in the image, and then pre-processing and cleansing of the collected data, feature selection of the human image and then building the model for classifier. In this research and implementation of skin pixels classifier models are proposed with their comparative performance analysis. The definition of the feature vector is simply the selection of skin pixels from the human image or stack of human images. The performance is evaluated by comparing and analysing skin colour segmentation algorithms. During the course of research implementation, efforts are iterative which help in selection of optimized skin classifier based on the machine learning algorithms and their performance analysis.
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVMsipij
The biometric is used to identify a person effectively and employ in almost all applications of day to day
activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform
(DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person
into one image using averaging technique is introduced to reduce execution time and memory. The DWT is
applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients
are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
ZERNIKE MOMENT-BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL T...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The face recognition system is critical when individuals have very similar biometric signature such
as identical twins. In this paper, new efficient facial-based identical twins recognition is proposed
according to geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
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A face recognition system using convolutional feature extraction with linear...IJECEIAES
Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of
pixels.There are manychallenges that affect the performance of face recognitionincluding illumination
variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the
illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and
classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity
Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For
feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support
Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the
AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification
methods, experiments involving various block sizes are conducted to assess the computation performance
and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved
with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2
block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm
performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function
performs extremely well on theAT&Tdataset for both holistic and block-based approaches.
PERFORMANCE EVALUATION OF BLOCK-SIZED ALGORITHMS FOR MAJORITY VOTE IN FACIAL ...ijaia
Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of
pixels.There are manychallenges that affect the performance of face recognitionincluding illumination
variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the
illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and
classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity
Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For
feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support
Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the
AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification
methods, experiments involving various block sizes are conducted to assess the computation performance
and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved
with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2
block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm
performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function
performs extremely well on theAT&Tdataset for both holistic and block-based approaches
PERFORMANCE EVALUATION OF BLOCK-SIZED ALGORITHMS FOR MAJORITY VOTE IN FACIAL ...gerogepatton
Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of
pixels.There are manychallenges that affect the performance of face recognitionincluding illumination
variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the
illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and
classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity
Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For
feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support
Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the
AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification
methods, experiments involving various block sizes are conducted to assess the computation performance
and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved
with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2
block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm
performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function
performs extremely well on theAT&Tdataset for both holistic and block-based approaches.
Implementation of Face Recognition in Cloud Vision Using Eigen FacesIJERA Editor
Cloud computing comes in several different forms and this article documents how service, Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The papers discuss a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed System is connection of two stages – Feature extraction using principle component analysis and recognition using the back propagation Network. This paper also discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The dispute lies with how to performance task partitioning from mobile devices to cloud and distribute compute load among cloud servers to minimize the response time given diverse communication latencies and server compute powers
National Flags Recognition Based on Principal Component Analysisijtsrd
Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26775/national-flags-recognition-based-on-principal-component-analysis/soe-moe-myint
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...MangaiK4
Abstract -Computer vision is a dynamic research field which involves analyzing, modifying, and high-level understanding of images. Its goal is to determine what is happening in front of a camera and use the facts understood to control a computer or a robot, or to provide the users with new images that are more informative or esthetical pleasing than the original camera images. It uses many advanced techniques in image representation to obtain efficiency in computation. Sparse signal representation techniqueshave significant impact in computer vision, where the goal is to obtain a compact high-fidelity representation of the input signal and to extract meaningful information. Segmentation and optimal parallel processing algorithms are expected to further improve the efficiency and speed up in processing.
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...MangaiK4
Abstract -Computer vision is a dynamic research field which involves analyzing, modifying, and high-level understanding of images. Its goal is to determine what is happening in front of a camera and use the facts understood to control a computer or a robot, or to provide the users with new images that are more informative or esthetical pleasing than the original camera images. It uses many advanced techniques in image representation to obtain efficiency in computation. Sparse signal representation techniqueshave significant impact in computer vision, where the goal is to obtain a compact high-fidelity representation of the input signal and to extract meaningful information. Segmentation and optimal parallel processing algorithms are expected to further improve the efficiency and speed up in processing.
ZERNIKE MOMENT-BASED FEATURE EXTRACTION FOR FACIAL RECOGNITION OF IDENTICAL T...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The face recognition system is critical when individuals have very similar biometric signature such
as identical twins. In this paper, new efficient facial-based identical twins recognition is proposed
according to geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled and rotated facial images of identical twins in different illuminations.
The results prove the ability of proposed method to recognize a pair of identical twins. Also, results show
that the proposed method is robust to rotation, scaling and changing illumination.
& Topics International Journal of Computer Science, Engineering and Informati...ijcseit
Face recognition is one of the most challenging problems in the domain of image processing and machine
vision. The face recognition system is critical when individuals have very similar biometric signature such
as identical twins. In this paper, new efficient facial-based identical twins recognition is proposed
according to geometric moment. The utilized geometric moment is Zernike Moment (ZM) as a feature
extractor inside the facial area of identical twins images. Also, the facial area in an image is detected using
AdaBoost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian
Twin Society which contain scaled and rotated facial images of identical twins in different illuminations.
The results prove the ability of proposed method to recognize a pair of identical twins. Also, results show
that the proposed method is robust to rotation, scaling and changing illumination.
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.
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Independent Component Analysis of Edge Information for Face Recognition
1. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 120
Independent Component Analysis of Edge Information
for Face Recognition
Kailash J. Karande kailashkarande@yahoo.co.in
Department of Information Technology,
Sinhgad Institute of Technology, Lonavala,
Dist-Pune, Maharashtra State. (India) 410401.
Sanjay N. Talbar sntalbar@yahoo.com
Department of Electronics & Telecommunication
SGGS Institute of Engineering &Technology, Nanded,
Maharashtra State, India.
Abstract
In this paper we address the problem of face recognition using edge information
as independent components. The edge information is obtained by using
Laplacian of Gaussian (LoG) and Canny edge detection methods then
preprocessing is done by using Principle Component analysis (PCA) before
applying the Independent Component Analysis (ICA) algorithm for training of
images. The independent components obtained by ICA algorithm are used as
feature vectors for classification. The Euclidean distance and Mahalanobis
distance classifiers are used for testing of images. The algorithm is tested on two
different databases of face images for variation in illumination and facial poses
up to 1800rotation angle.
Keywords: Principle Component analysis (PCA), Independent Component Analysis (ICA), Laplacian of
Gaussian ( LoG) and Canny edge detection Euclidean distance classifier, Mahalanobis distance classifier.
1. INTRODUCTION
Face recognition is a task that humans perform routinely and effortlessly in their daily
lives. Wide availability of powerful and low-cost desktop and embedded computing
systems has created an enormous interest in automatic processing of digital images and
videos in a number of applications, including biometric authentication, surveillance,
human-computer interaction, and multimedia management. Research and development
in automatic face recognition follows naturally.
Research in face recognition is motivated not only by the fundamental challenges this
recognition problem poses but also by numerous practical applications where human
identification is needed. Face recognition, as one of the primary biometric technologies,
became more and more important owing to rapid advances in technologies such as
digital cameras, the Internet and mobile devices, and increased demands on security.
Face recognition has several advantages over other biometric technologies: It is natural,
non intrusive, and easy to use. Among the six biometric attributes considered by
Hietmeyer [1], facial features scored the highest compatibility in a Machine Readable
Travel Documents (MRTD) [2] system based on a number of evaluation factors, such as
enrollment, renewal, machine requirements, and public perception.
2. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 121
A face recognition system is expected to identify faces present in images and videos
automatically. It can operate in either or both of two modes: (1) face verification (or
authentication), and (2) face identification (or recognition). Face verification involves a
one-to-one match that compares a query face image against a template face image
whose identity is being claimed. Face identification involves one-to-many matches that
compare a query face image against all the template images in the database to
determine the identity of the query face.
There are two predominant approaches to the face recognition problem: geometric
(feature based) and photometric (view based). As a researcher interest in face
recognition continued and many different methods are proposed, like well studied
methods using Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA)
and Elastic Bunch Graph Matching (EBGM). In order to organize the vast field of face
recognition, several approaches are conceivable. For instance, algorithms treating the
face and its environment as uncontrolled systems could be distinguished from systems
that control the lighting or background of the scene, or the orientation of the face. Or
systems that use one or more still images for the recognition task could be distinguished
from others that base their efforts on video sequences.
The comparison of face recognition using PCA and ICA on FERET database with
different classifiers [3] [4] are discussed and found that the ICA has better recognition
rate as compared with PCA with statistically independent basis images and also with
statistically independent coefficients. Their findings are based on the frontal face image
datasets are encouraging with few face expressions. Marian S Bartlett used version of
ICA [5] derived from the principle of optimal information transfer through sigmoidal
neurons on face images from FERET database has proved that ICA representation gave
the best performance on the frontal face images. Feature selection in the independent
component subspace [6] which gives the benefits for face recognition with changes in
illumination and facial expressions. Fusion of ICA features like Spatial, Temporal and
Localized features [7] [8] for Face Recognition are considered as optimization method.
An independent Gabor features (IGFs) method and its application to face recognition
using ICA [9] is discussed by Chengjun Liu; Wechsler, H. The novelty of the IGF method
comes from 1) the derivation of independent Gabor features in the feature extraction
stage and 2) the development of an IGF features-based probabilistic reasoning model
(PRM) classification method in the pattern recognition stage. In particular, the IGF
method first derives a Gabor feature vector from a set of down sampled Gabor wavelet
representations of face images, then reduces the dimensionality of the vector by means
of principal component analysis, and finally defines the independent Gabor features
based on the independent component analysis (ICA).
The illumination has great influence on how a face image looks. Researchers have
proved that for a face image, the difference caused by illumination changes has even
exceeded the difference caused by identity changes [10]. The big challenge of face
recognition lies in dealing with variations of pose, illumination, and expression. Also there
is need to address the problem of identity changes using structural components.
In this paper we propose the face recognition using Edge information and ICA with large
rotation angles with poses and variation in illumination conditions. Here we used the edge
information of face images using different standard edge detection function operators like
canny, Laplacian of Gaussian (log). The edge information is being used to calculate
independent components for face recognition. We used the database which has the large
rotation angles up to 180
0
change between the images of person while looking right and
or left. We considered the face images having various orientations of the face i.e.: looking
front, looking left, looking right, looking up, looking up towards left, looking up towards
right, looking down. In this study we considered the samples of individual person which
3. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 122
consist of sufficient number of images having expressions, changes in illumination and
large rotation angles. For illumination variation the effects of light on face image from left,
right, top and bottom sides are considered. The paper is organized as follows. In Section
2 we introduce the ICA and information about edge detection in section 3. The section 4
specifies the need of the preprocessing before applying the ICA algorithm. The modified
Fast ICA algorithm is presented in the Section 5 and classifiers are discussed in section
6. Experimental results are discussed in Section 7 and accordingly the conclusions are
drawn in Section 8.
2. Introduction to ICA
ICA will be rigorously defined as a statistical ‘latent variable’ model. Assume that we
observe n linear mixtures x1, ....., xn of n independent components
njnjjj sasasax +++= ......2211 For all j. (1)
we have now dropped the time index t; in the ICA model, we assume that each mixture xj
as well as each independent component sk is a random variable, instead of a proper time
signal. The observed values xj(t), e.g. the microphone signals in the cocktail party
problem, are then a sample of this random variable. Without loss of generality, we can
assume that both the mixture variables and the independent components have zero
mean. If this is not true, then the observable variables xi can always be centered by
subtracting the sample mean, which makes the model zero-mean.
It is convenient to use vector-matrix notation instead of the sums like in the previous
equation. Let us denote by x the random vector whose elements are the mixtures x1, ...,
xn. And likewise by s the random vector with elements s1, ..., sn. Let us denote by A the
matrix with elements aij. Generally, bold lower case letters indicate vectors and bold
upper-case letters denote matrices. All vectors are understood as column vectors; thus x
T
or the transpose of x, is a row vector. Using this vector-matrix notation, the above mixing
model is written as
x =As (2)
Sometimes we need the columns of matrix A; denoting them by aj the model can also be
written as,
x = ∑=
n
i 1
aisi (3)
The statistical model in equation.(2) is called independent component analysis or ICA
model[11]. The ICA model is a generative model which means that it describes how the
observed data are generated by a process of mixing the components si. The independent
components are latent variables, meaning that they cannot be directly observed. Also the
mixing matrix is assumed to be unknown. All we observe is the random vector x, and we
must estimate both A and s using it. This must be done under as general assumptions as
possible.
The starting point for ICA is the very simple assumption that the components si are
statistically independent. It will be seen that we also assume that the independent
component must have nongaussian distributions. However, in the basic model we do not
assume these distributions known (if they are known, the problem is considerably
simplified). For simplicity, we are also assuming that the unknown mixing matrix is
square, but this assumption can be sometimes relaxed. Then, after estimating the matrix
A, we can compute its inverse, say W, and obtain the independent component simply by:
s = Wx. (4)
4. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 123
ICA is very closely related to the method called blind source separation (BSS) or blind
signal separation. A “source” means here an original signal, i.e. independent component,
like the speaker in a cocktail party problem. “Blind” means that we know very little, if
anything, on the mixing matrix, and make little assumptions on the source signals. ICA is
one method perhaps the most widely used, for performing blind source separation.
3. Edge Detection
Edges are boundaries between different textures. Edge also can be defined as
discontinuities in image intensity from one pixel to another. The edges for an image are
always the important characteristics that offer an indication for a higher frequency.
Detection of edges for an image may help for image segmentation, data compression, and
also help for well matching, such as image reconstruction and so on.
Laplacian of a Gaussian (LoG) Detector: Consider the Gaussian function
2
2
2
)( σ
r
erh
−
−= (5)
where r
2
=x
2
+y
2
and σ is the standard deviation. This is smoothing function which, if
convolved with an image, will blur it. The degree of blurring is determined by the value of σ.
The Laplacian function [13] is
2
2
2
4
22
2
)( σ
σ
σ
r
e
r
rh
−
−
−=∇ (6)
For obvious reason, this function is called the Laplacian of a Gaussian (LoG). Because of
the second derivative is a linear operation, convolving an image with )(2
rh∇ is the same
as convolving the image with the smoothing function first and then computing the Laplacian
of the result. This is the key concept underlying the LoG detector.
Canny Edge Detection:
Finds edge by looking for local maxima of the gradient of f(x, y). The gradient is calculated
using the derivative of a Gaussian filter. The method uses two thresholds to detect strong
and weak edges and includes the weak edges at the output only if they are connected to
strong edges. Therefore this method is more likely to detect true weak edges. The Canny
edge detector[13] is the most powerful edge detector provided by function edge. In this
method the image is smoothed using a Gaussian filter with a specified standard deviation,
σ, to reduce noise. The local gradient,
[ ] 2/122
),( yGxGyxg += (7)
and edge direction,
)/(tan),( 1
GxGyyx −
=α (8)
are computed at each point. Gx and Gy computed using sobel, prewitt or Roberts method
of edge detection. An edge point is defined to be a point whose strength is locally maximum
in the direction of the gradient.
4. Preprocessing by PCA
There are several approaches for the estimation of the independent component analysis
(ICA) model. In particular, several algorithms were proposed for the estimation of the basic
version of the ICA model, which has a square mixing matrix and no noise. Practically when
applying the ICA algorithms to real data, some practical considerations arise and need to
be taken into account. To overcome these practical considerations we implemented a
preprocessing technique in this algorithm that is dimension reduction by principal
5. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 124
component analysis. That may be useful and even necessary before the application of the
ICA algorithms in practice. Overall face recognition benefits from feature selection of PCA
and ICA combination [6].
A common preprocessing technique for multidimensional data is to reduce its dimension
by principal component analysis (PCA) [3]. Basically, the data is projected linearly onto a
subspace
xEX n=
~
(9)
so that the maximum amount of information (in the least-squares sense) is preserved.
Reducing dimension in this way has several benefits. First, let us consider the case
where the number of independent components (ICs) n is smaller than the number of
mixtures; say m. Performing ICA on the mixtures directly can cause big problems in such
a case, since the basic ICA model does not hold anymore. Using PCA we can reduce the
dimension of the data to n. After such a reduction, the number of mixtures and ICs are
equal, the mixing matrix is square, and the basic ICA model holds.
The question is whether PCA is able to find the subspace correctly, so that the n ICs can
be estimated from the reduced mixtures. This is not true in general, but in a special case
it turns out to be the case. If the data consists of n ICs only, with no noise added, the
whole data is contained in an n-dimensional subspace. Using PCA for dimension
reduction clearly finds this n-dimensional subspace, since the eigenvalues corresponding
to that subspace, and only those eigenvalues, are nonzero. Thus reducing dimension
with PCA works correctly. In practice, the data is usually not exactly contained in the
subspace, due to noise and other factors, but if the noise level is low, PCA still finds
approximately the right subspace. In the general case, some weak ICs may be lost in the
dimension reduction process, but PCA may still be a good idea for optimal estimation of
the strong ICs.
5. ICA Algorithm
Here we used the modified FastICA algorithm [11] for computing of independent
components. Before computing the independent components we use to calculate the
principle components and then whitened those components to reduce the size of matrix. In
the previous section we have seen the need to use PCA before applying ICA. After finding
principle components we whiten the eigenvector matrix to reduce the size of matrix and
make it square. To estimate several independent components, we need to run the ICA
algorithm several times with weight vectors w1, ...,wn. To prevent different vectors from
converging to the same maxima we must decorrelates the outputs w
T
1x, ...,w
T
n x after
every iteration.
A simple way of achieving decorrelation is a deflation scheme based on a Gram-Schmidt-
like decorrelation[13]. This means that we estimate the independent components one by
one. When we have estimated p independent components, or p vectors w1, ...,wp, we run
the one-unit fixed-point algorithm for wp+1, and after every iteration step subtract from wp+1
the projections w
T
p+1 wj wj , j = 1, ..., p of the previously estimated p vectors, and then
renormalize wp+1:
∑=
+++ −=
p
j
jjp
T
pp WWWWW
1
111 (10)
1111 / ++++ = pp
T
pp WWWW (11)
6. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 125
In certain applications, however, it may be desired to use a symmetric decorrelation, in
which no vectors are privileged over others. This can be accomplished, e.g., by the
classical method involving matrix square roots,
WWWW T 2/1
)( −
= (12)
where W is the matrix (w1, ...,wn)
T
of the vectors, and the inverse square root (WW
T
)
−1/2
is obtained from the eigenvalues decomposition of WW
T
= FDF
T
as (WW
T
)
−1/2
= FD
−1/2
F
T
. A simpler alternative is the following iterative algorithm,
||||/ T
WWWW = (13)
As we are using the ICA algorithm the training time required is more as compared to
other methods. For the algorithm developed in this paper required around few seconds time
as training time.
6. Similarity Metrics using Classifiers
As we are using the ICA algorithm the feature extraction time required is more as
compared to other methods. For the algorithm developed in this paper required around few
seconds time as extraction time. The query image will be more similar to the database
images if the distance is smaller. For similarity measurement we used the Euclidean
distance classifier and Mahalanobis distance classifier [12][14], for calculating the minimum
distance between the query image and images to be matched from the database. If x
and y are the two d-dimensional feature vectors of the database image and query image
respectively, then these distance metrics are defined as follows. Euclidean distance to
determine closeness reduces the problem to computing the distance measures:
∑=
−=
d
i
iiE yxyxd
1
2
)(),( (14)
If the distance is small, we say the images are similar and we can decide which the most
similar images in the database are. Another distance metrics for comparison of the
retrieval of images used is Mahalanobis metric:
)()()(),( 12
yxxCovyxyxdMah −−= −
(15)
The results of these classifiers are very much close to each other. In Mahalanobis
metrics the time required for similarity measure is more due to involvement of the
covariance matrix.
7. Experimental Results
The experimental results presented in this paper are divided in to two parts. Both parts
evaluate the face representation using Laplacian of Gaussian ( LoG) and Canny edge
detection for feature extraction methods. In first part the face images having variation in
illumination conditions are used. In second part face images are used with large rotation
angles. There are four stages of the algorithm developed in MATLAB environment. In the first
stage we calculate the edge information using LoG or Canny edge detection method. The
7. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 126
second stage is used for preprocessing of image matrix using PCA for dimension reduction.
Third stage is used to find out independent components as feature vectors using ICA
algorithm. In last stage we used different two classifiers for testing of input image to be
recognized.
Figure 1: Various view of face images with different illumination conditions in Asian face database
Two standard databases are used for evaluation of these results. The first database is
published by IIT Kanpur [16] widely used for research purpose known as Indian face
database. In this database images of 60 persons with 10 sample images with different
orientations and views are available. The second database known as Asian face image
database [15] is from Intelligent Multimedia research laboratories having face images of 56
male persons with 10 samples each; which consist of variation in illumination conditions and
different views. The resolution of all images we used in the algorithm is 128 x128 for
computational purpose. Few face images are shown in Figure 1 and Figure 4 from both the
databases with various views.
In this study we have explored feature selection techniques using Edge information and ICA
for face recognition. Feature selection techniques are warranted especially for ICA features,
since these are devoid of any importance ranking based on energy content. The study is
carried out on the face database that contains both facial expression with pose variation and
illumination variations. We implemented the algorithms for face recognition using Edge
information and ICA on the different conditions of face images with different set of images.
These set of images we used are by selecting the first 50, 100, 150 or 200 independent
components.
8. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 127
Figure 2: Edge information of few face images
The edge detected images are shown in Figure 2 and are then preprocessed by PCA.
The independent components are obtained by ICA algorithm and few are shown in Figure
3. These are the independent components obtained for face images with variation in
illumination. The light condition on the face images are from left and right sides as well as
from top and down directions. Under different illumination conditions the results are
encouraging as shown in table 1.The images used in this part are from Asian face
database with different illumination conditions as shown above in Figure 1. For training
we used different sets of independent components varying from 50 to 200. The edge
information for the images with large rotation angles are shown in Figure 5 and
corresponding few independent components are shown in Figure 6. For this part we used
images from Indian face database with large rotation angle up to 180
0
.
Figure 3: Independent Components of few face images
9. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 128
Figure 4: Various view of face images with different face orientations in Indian face database
Figure 5: Edge information of few face images
Figure 6: Independent Components of few face images
10. Kailash J.Karande & Sanjay N Talbar
International Journal of Image Processing (IJIP) Volume (3) : Issue (3) 129
Table 1: Results of the face recognition algorithm
8. Conclusion
In this paper an independent component analysis of Edge information of face images has
been discussed and used for face recognition. Two standard databases are used, which
contains the face images with different orientations, expressions and change in illumination.
The performance of the algorithm suggested produces very good recognition rate varying
from 74% to 95%. Applying ICA on face images for recognition; selection of ICs are
required in order to give better performance. We used two different approaches for edge
detection where Canny edge detector has given better results as compared to LoG edge
detector. We adopted modified FastICA algorithm for computing the independent
components. If we observe the results of face recognition using ICA under variation of
illumination conditions the results are very good and encouraging. The results of the first
part with ICA method are also close to the results of second part. This implies that ICA for
edge information of face images is less sensitive to illumination change as compared to
face orientations as shown in results.
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