This document summarizes a research paper on face recognition using Gabor features and PCA. It begins with an introduction to face recognition and discusses challenges like lighting, pose, and orientation. It then describes how the proposed system uses Gabor wavelets for preprocessing to reduce variations from pose, lighting, etc. Principal component analysis (PCA) is used to extract low dimensional and discriminating feature vectors from the preprocessed images. These feature vectors are then used for classification with k-nearest neighbors. The proposed system was tested on the Yale face database containing 100 images of 10 subjects with variable illumination and expressions.
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.
Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters IJERA Editor
The Biometrics is used to recognize a person effectively compared to traditional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Complex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian distance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.
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.
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.
MSB based Face Recognition Using Compression and Dual Matching TechniquesCSCJournals
Biometrics are used in almost all communication technology applications for secure recognition. In this paper, we propose MSB based face recognition using compression and dual matching techniques. The standard available face images are considered to test the proposed method. The novel concept of considering only four Most Significant Bits (MSB) of each pixel on image is introduced to reduce the total number of bits to half of an image for high speed computation and less architectural complexity. The Discrete Wavelet Transform (DWT) is applied to an image with only MSB's, and consider only LL band coefficients as final features. The features of the database and test images are compared using Euclidian Distance (ED) an Artificial Neural Network (ANN) to test the performance of the pot method. It is observed that, the performance of the proposed method is better than the existing methods.
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.
Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters IJERA Editor
The Biometrics is used to recognize a person effectively compared to traditional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Complex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian distance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.
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.
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.
MSB based Face Recognition Using Compression and Dual Matching TechniquesCSCJournals
Biometrics are used in almost all communication technology applications for secure recognition. In this paper, we propose MSB based face recognition using compression and dual matching techniques. The standard available face images are considered to test the proposed method. The novel concept of considering only four Most Significant Bits (MSB) of each pixel on image is introduced to reduce the total number of bits to half of an image for high speed computation and less architectural complexity. The Discrete Wavelet Transform (DWT) is applied to an image with only MSB's, and consider only LL band coefficients as final features. The features of the database and test images are compared using Euclidian Distance (ED) an Artificial Neural Network (ANN) to test the performance of the pot method. It is observed that, the performance of the proposed method is better than the existing methods.
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.
Perceptual Weights Based On Local Energy For Image Quality AssessmentCSCJournals
This paper proposes an image quality metric that can effectively measure the quality of an image that correlates well with human judgment on the appearance of the image. The present work adds a new dimension to the structural approach based full-reference image quality assessment for gray scale images. The proposed method assigns more weight to the distortions present in the visual regions of interest of the reference (original) image than to the distortions present in the other regions of the image, referred to as perceptual weights. The perceptual features and their weights are computed based on the local energy modeling of the original image. The proposed model is validated using the image database provided by LIVE (Laboratory for Image & Video Engineering, The University of Texas at Austin) based on the evaluation metrics as suggested in the video quality experts group (VQEG) Phase I FR-TV test.
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 ILLUMINATION INVARIANT FACE RECOGNITION USING 2D DISCRETE COSINE TRANSFORM...ijcsit
Automatic face recognition performance is affected due to the head rotations and tilt, lighting intensity and
angle, facial expressions, aging and partial occlusion of face using Hats, scarves, glasses etc.In this paper,
illumination normalization of face images is done by combining 2D Discrete Cosine Transform and
Contrast Limited Adaptive Histogram Equalization. The proposed method selects certain percentage of
DCT coefficients and rest is set to 0. Then, inverse DCT is applied which is followed by logarithm
transform and CLAHE. Thesesteps create illumination invariant face image, termed as ‘DCT CLAHE’
image. The fisher face subspace method extracts features from ‘DCT CLAHE’ imageand features are
matched with cosine similarity. The proposed method is tested in AR database and performance measures
like recognition rate, Verification rate at 1% FAR and Equal Error Rate are computed. The experimental
results shows high recognition rate in AR database.
Blind Source Separation of Super and Sub-Gaussian Signals with ABC AlgorithmIDES Editor
Recently, several techniques have been presented
for blind source separation using linear or non-linear mixture
models. The problem is to recover the original source signals
without knowing apriori information about the mixture model.
Accordingly, several statistic and information theory-based
objective functions are used in literature to estimate the
original signals without providing mixture model. Here,
swarm intelligence played a major role to estimate the
separating matrix. In our work, we have considered the recent
optimization algorithm, called Artificial Bee Colony (ABC)
algorithm which is used to generate the separating matrix in
an optimal way. Here, Employee and onlooker bee and scout
bee phases are used to generate the optimal separating matrix
with lesser iterations. Here, new solutions are generated
according to the three major considerations such as, 1) all
elements of the separating matrix should be changed according
to best solution, 2) individual element of the separating matrix
should be changed to converge to the best optimal solution, 3)
Random solution should be added. These three considerations
are implemented in ABC algorithm to improve the
performance in Blind Source Separation (BSS). The
experimentation has been carried out using the speech signals
and the super and sub-Gaussian signal to validate the
performance. The proposed technique was compared with
Genetic algorithm in signal separation. From the result, it
was observed that ABC technique has outperformed existing
GA technique by achieving better fitness values and lesser
Euclidean distance.
Face detection using the 3 x3 block rank patterns of gradient magnitude imagessipij
Face detection locates faces prior to various face-
related applications. The objective of face detecti
on is to
determine whether or not there are any faces in an
image and, if any, the location of each face is det
ected.
Face detection in real images is challenging due to
large variability of illumination and face appeara
nces.
This paper proposes a face detection algorithm usin
g the 3×3 block rank patterns of gradient magnitude
images and a geometrical face model. First, the ill
umination-corrected image of the face region is obt
ained
using the brightness plane that is produced using t
he locally minimum brightness of each block. Next,
the
illumination-corrected image is histogram equalized
, the face region is divided into nine (3×3) blocks
, and
two directional (horizontal and vertical) gradient
magnitude images are computed, from which the 3×3
block rank patterns are obtained. For face detectio
n, using the FERET and GT databases three types of
the
3×3 block rank patterns are a priori determined as
templates based on the distribution of the sum of t
he
gradient magnitudes of each block in the face candi
date region that is also composed of nine (3×3) blo
cks.
The 3×3 block rank patterns roughly classify whethe
r the detected face candidate region contains a fac
e or
not. Finally, facial features are detected and used
to validate the face model. The face candidate is
validated as a face if it is matched with the geome
trical face model. The proposed algorithm is tested
on the
Caltech database images and real images. Experiment
al results with a number of test images show the
effectiveness of the proposed algorithm.
In this paper, an attempt has been made to extract texture
features from facial images using an improved method of
Illumination Invariant Feature Descriptor. The proposed local
ternary Pattern based feature extractor viz., Steady Illumination
Local Ternary Pattern (SIcLTP) has been used to extract texture
features from Indian face database. The similarity matching
between two extracted feature sets has been obtained using Zero
Mean Sum of Squared Differences (ZSSD). The RGB facial images
are first converted into the YIQ colour space to reduce the
redundancy of the RGB images. The result obtained has been
analysed using Receiver Operating Characteristic curve, and is
found to be promising. Finally the results are validated with
standard local binary pattern (LBP) extractor.
Comparative Analysis of Partial Occlusion Using Face Recognition TechniquesCSCJournals
This paper presents a comparison of partial occlusion using face recognition techniques that gives in which technique produce better result for total success rate. The partial occlusion of face recognition is especially useful for people where part of their face is scarred and defect thus need to be covered. Hence, either top part/eye region or bottom part of face will be recognized respectively. The partial face information are tested with Principle Component Analysis (PCA), Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF). The comparative results show that the recognition rate of 95.17% with r = 80 by using SFNMF for bottom face region. On the other hand, eye region achieves 95.12% with r = 10 by using LNMF.
A novel approach for efficient skull stripping using morphological reconstruc...eSAT Journals
Abstract Brain is the part of the central nervous system located in skull. For the diagnosis of human brain bearing tumour, skull stripping plays an important pre-processing role. Skull stripping is the process separating brain and non-brain tissues of the head which is the critical processing step in the analysis of neuroimaging data. Though various algorithms have been proposed to address this problem, challenges remain. In this paper a new efficient skull stripping method for magnetic resonance images (MRI) is proposed. This method adopts a two-step approach; in the first step an improved systematic application of morphological reconstructions operations is done for the brain image and in the second step, a thresholding based technique is used to extract the brain inside the skull. This paper experimented on Axial PD and FLAIR MRI brain images. Index Terms: Skull stripping, thresholding, morphological reconstruction, Axial PD and FLAIR MRI images of brain.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
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.
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.
Perceptual Weights Based On Local Energy For Image Quality AssessmentCSCJournals
This paper proposes an image quality metric that can effectively measure the quality of an image that correlates well with human judgment on the appearance of the image. The present work adds a new dimension to the structural approach based full-reference image quality assessment for gray scale images. The proposed method assigns more weight to the distortions present in the visual regions of interest of the reference (original) image than to the distortions present in the other regions of the image, referred to as perceptual weights. The perceptual features and their weights are computed based on the local energy modeling of the original image. The proposed model is validated using the image database provided by LIVE (Laboratory for Image & Video Engineering, The University of Texas at Austin) based on the evaluation metrics as suggested in the video quality experts group (VQEG) Phase I FR-TV test.
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 ILLUMINATION INVARIANT FACE RECOGNITION USING 2D DISCRETE COSINE TRANSFORM...ijcsit
Automatic face recognition performance is affected due to the head rotations and tilt, lighting intensity and
angle, facial expressions, aging and partial occlusion of face using Hats, scarves, glasses etc.In this paper,
illumination normalization of face images is done by combining 2D Discrete Cosine Transform and
Contrast Limited Adaptive Histogram Equalization. The proposed method selects certain percentage of
DCT coefficients and rest is set to 0. Then, inverse DCT is applied which is followed by logarithm
transform and CLAHE. Thesesteps create illumination invariant face image, termed as ‘DCT CLAHE’
image. The fisher face subspace method extracts features from ‘DCT CLAHE’ imageand features are
matched with cosine similarity. The proposed method is tested in AR database and performance measures
like recognition rate, Verification rate at 1% FAR and Equal Error Rate are computed. The experimental
results shows high recognition rate in AR database.
Blind Source Separation of Super and Sub-Gaussian Signals with ABC AlgorithmIDES Editor
Recently, several techniques have been presented
for blind source separation using linear or non-linear mixture
models. The problem is to recover the original source signals
without knowing apriori information about the mixture model.
Accordingly, several statistic and information theory-based
objective functions are used in literature to estimate the
original signals without providing mixture model. Here,
swarm intelligence played a major role to estimate the
separating matrix. In our work, we have considered the recent
optimization algorithm, called Artificial Bee Colony (ABC)
algorithm which is used to generate the separating matrix in
an optimal way. Here, Employee and onlooker bee and scout
bee phases are used to generate the optimal separating matrix
with lesser iterations. Here, new solutions are generated
according to the three major considerations such as, 1) all
elements of the separating matrix should be changed according
to best solution, 2) individual element of the separating matrix
should be changed to converge to the best optimal solution, 3)
Random solution should be added. These three considerations
are implemented in ABC algorithm to improve the
performance in Blind Source Separation (BSS). The
experimentation has been carried out using the speech signals
and the super and sub-Gaussian signal to validate the
performance. The proposed technique was compared with
Genetic algorithm in signal separation. From the result, it
was observed that ABC technique has outperformed existing
GA technique by achieving better fitness values and lesser
Euclidean distance.
Face detection using the 3 x3 block rank patterns of gradient magnitude imagessipij
Face detection locates faces prior to various face-
related applications. The objective of face detecti
on is to
determine whether or not there are any faces in an
image and, if any, the location of each face is det
ected.
Face detection in real images is challenging due to
large variability of illumination and face appeara
nces.
This paper proposes a face detection algorithm usin
g the 3×3 block rank patterns of gradient magnitude
images and a geometrical face model. First, the ill
umination-corrected image of the face region is obt
ained
using the brightness plane that is produced using t
he locally minimum brightness of each block. Next,
the
illumination-corrected image is histogram equalized
, the face region is divided into nine (3×3) blocks
, and
two directional (horizontal and vertical) gradient
magnitude images are computed, from which the 3×3
block rank patterns are obtained. For face detectio
n, using the FERET and GT databases three types of
the
3×3 block rank patterns are a priori determined as
templates based on the distribution of the sum of t
he
gradient magnitudes of each block in the face candi
date region that is also composed of nine (3×3) blo
cks.
The 3×3 block rank patterns roughly classify whethe
r the detected face candidate region contains a fac
e or
not. Finally, facial features are detected and used
to validate the face model. The face candidate is
validated as a face if it is matched with the geome
trical face model. The proposed algorithm is tested
on the
Caltech database images and real images. Experiment
al results with a number of test images show the
effectiveness of the proposed algorithm.
In this paper, an attempt has been made to extract texture
features from facial images using an improved method of
Illumination Invariant Feature Descriptor. The proposed local
ternary Pattern based feature extractor viz., Steady Illumination
Local Ternary Pattern (SIcLTP) has been used to extract texture
features from Indian face database. The similarity matching
between two extracted feature sets has been obtained using Zero
Mean Sum of Squared Differences (ZSSD). The RGB facial images
are first converted into the YIQ colour space to reduce the
redundancy of the RGB images. The result obtained has been
analysed using Receiver Operating Characteristic curve, and is
found to be promising. Finally the results are validated with
standard local binary pattern (LBP) extractor.
Comparative Analysis of Partial Occlusion Using Face Recognition TechniquesCSCJournals
This paper presents a comparison of partial occlusion using face recognition techniques that gives in which technique produce better result for total success rate. The partial occlusion of face recognition is especially useful for people where part of their face is scarred and defect thus need to be covered. Hence, either top part/eye region or bottom part of face will be recognized respectively. The partial face information are tested with Principle Component Analysis (PCA), Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF). The comparative results show that the recognition rate of 95.17% with r = 80 by using SFNMF for bottom face region. On the other hand, eye region achieves 95.12% with r = 10 by using LNMF.
A novel approach for efficient skull stripping using morphological reconstruc...eSAT Journals
Abstract Brain is the part of the central nervous system located in skull. For the diagnosis of human brain bearing tumour, skull stripping plays an important pre-processing role. Skull stripping is the process separating brain and non-brain tissues of the head which is the critical processing step in the analysis of neuroimaging data. Though various algorithms have been proposed to address this problem, challenges remain. In this paper a new efficient skull stripping method for magnetic resonance images (MRI) is proposed. This method adopts a two-step approach; in the first step an improved systematic application of morphological reconstructions operations is done for the brain image and in the second step, a thresholding based technique is used to extract the brain inside the skull. This paper experimented on Axial PD and FLAIR MRI brain images. Index Terms: Skull stripping, thresholding, morphological reconstruction, Axial PD and FLAIR MRI images of brain.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
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.
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
A novel approach for performance parameter estimation of face recognition bas...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
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
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emotional facial expression recognition.
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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
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SYMMETRICAL WEIGHTED SUBSPACE HOLISTIC APPROACH FOR EXPRESSION RECOGNITIONijcsit
Human face expression is one of the cognitive activity or attribute to deliver the opinions to others.This paper mainly delivers the performance of appearance based holistic approach subspace methods based on Principal Component Analysis (PCA). In this work texture features are extracted from face images using Gabor filter. It was observed that extracted texture feature vector space has higher dimensional and has
more number of redundant contents. Hence training, testing and classification time becomes more. The expression recognition accuracy rate is also reduced. To overcome this problem Symmetrical Weighted 2DPCA (SW2DPCA) subspace method is introduced. Extracted feature vector space is projected in to subspace by using SW2DPCA method. By implementing weighted principles on odd and even symmetrical
decomposition space of training samples sets proposed method have been formed. Conventional PCA and 2DPCA method yields less recognition rate due to larger variations in expressions and light due to more number of feature space redundant variants. Proposed SW2DPCA method optimizes this problem by reducing redundant contents and discarding unequal variants. In this work a well known JAFFE databases
is used for experiments and tested with proposed SW2DPCA algorithm. From the experimental results it was found that facial recognition accuracy rate of GF+SW2DPCA based feature fusion subspace method has been increased to 95.24% compared to 2DPCA method.
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Independent Component Analysis of Edge Information for Face RecognitionCSCJournals
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H0334749
1. IOSR Journal of Computer Engineering (IOSRJCE)
ISSN: 2278-0661 Volume 3, Issue 3 (July-Aug. 2012), PP 47-49
www.iosrjournals.org
Face Recognition Using Gabor features And PCA
1
Miss. Rupali mhaske, 2Miss. Jyoti Bedre, 3Ms. Shubhangi Sapkal
1
(Computer science & Engg. Department, RMCOE/JNT University, India)
2
(Computer science & Engg. Department, Govt. College/ Aurangabad University, India)
Abstract : Face recognition is a hot research topic in the fields of pattern recognition and computer vision,
which has been found a widely used in many applications, such as verification of credit card, security access
control, and human computer interface. As a result, numerous face recognition algorithms have been proposed,
and surveys in this area can be found. Although many approaches for face recognition have been proposed in
the past, none of them can overcome the main problem of lighting, pose and orientation. For a real time face
recognition system, these constraints are to be a major Analysis (PCA) .These methods challenge which has to
be addressed. In this proposed system, a methodology is given for improving the robustness of a face
recognition system based on two well-known statistical modelling methods to represent a face image: Principal
Component extract the discriminates features from the face. Preprocessing of human face image is done using
Gabor wavelets which eliminates the variations due to pose, lighting and features to some extent.
PCA extract low dimensional and discriminating feature vectors and these feature vectors were used for
classification. The classification stage uses nearest neighbour as classifier. This proposed system will use the
YALE face data base with 100 frontal images corresponding to10 different subjects of variable illumination and
facial expressions.
Keywords: Face recognition, Gabor Wavelet transform, Principal Component Analysis
I. INTRODUCTION
Face recognition has been a very popular research topic in recent years. The first attempts began in the
1960's with a semi-automated system. It used features such as eyes, ears, noses, and mouths. Then distances and
ratios were computed from these marks to a common reference point and compared to reference data. In the
early 1970's Goldstein, Harmon and Lesk created a system of 21 subjective markers such as hair color and lip
thickness. Later Kohonen demonstrated that a simple neural net could perform face recognition for aligned and
normalized face images. The type of network he employed computed a face description by approximating the
eigenvectors of the face image's autocorrelation matrix; these eigenvectors are now known as 'Eigenfaces'.
Kohonen's system was not a practical success, however, because of the need for precise alignment and
normalization [1]. Turk and Pentland (1991) then demonstrated that when we perform the coding using the
eigenfaces the residual error could be used both to detect faces in cluttered natural imagery, and to determine the
precise location and scale of faces in an image.
Fig. 1 Block Diagram of Face Recognition
An overview of the face recognition process is illustrated in Fig. 1. In the figure the gallery is the set of
known individuals. The images used to test the algorithms are called probes. A probe is either a new image of
individual in the gallery or an image of an individual not in the gallery. To compute performance, one needs
both a gallery and probe set. The probes are presented to an algorithm, and the algorithm returns the best match
between the each probe and images in the gallery. The estimated identity of a probe is the best match.
While there are many databases in use currently, the choice of an appropriate database to be used
should be made based on the task given [2]. Some face data sets that are commonly used: Color FERET
Database, Yale Face Database, and PIE Database, FIA video Database, CBCL Face recognition Database,
www.iosrjournals.org 47 | Page
2. Face Recognition Using Gabor features And PCA
Expression Image Database, Mugshot Identification Database, Indian Face Database, Face Recognition Data,
and University of Essex, UK.
II. GABOR WAVELETS
In face recognition system the feature based method finds the important features on the face and
represents them in an efficient way. Physiological studies found that the cells in the human virtual cortex can be
selectively tuned to orientation and to spatial frequency. This confirmation that the response of the simple cell
could be approximated using 2D gabor filters is given by G.Daugmann [3]. Gabor filters were introduced in
image processing because of their biological relevance and computational properties [4, 5]. The kernels of gabor
wavelets are similar to 2D receptive field profiles of the mammalian cortical simple cells. These kernels exhibit
desirable characteristics of orientation selectivity and spatial locality. The extraction of local features in an
image can be effectively done using gabor wavelets.Using Gabor wavelets is robust to illumination, poses and
facial expression changes. Considering all Gabor kernels,all the features are concatenated to form a single gabor
feature vector. Then this high dimensional gabor vector space is much reduced by applying statistical modeling
methods first PCA and then LDA to obtain more independent and discriminating features.
III. PRINCIPALCOMPONENT ANALYSIS
In image recognition and compression, Principal Component Analysis (PCA) is one of the most
successful techniques. The large dimensionality of the data space (observed variables) is reduced to the smaller
intrinsic dimensionality of feature space (independent variables).This is the case when there is a strong
correlation between observed variables and main purpose of using PCA. Using PCA it is capable of
transforming each original image of the training set into a corresponding eigenface. The reconstruction of any
original image from the training setby combining the eigenfaces is an important feature of PCA which are
nothing but characteristic features of the faces. Consequently by adding up all the eigenfaces in the right
proportion the original face image can be reconstructed. Each eigenface represents only certain features of the
face, which may or may not be present in the original image. If the particular feature is not present in the
original image, then the corresponding eigenface should contribute a smaller part to the sum of eigenfaces. If, by
contrary, the feature is present in the original image to a higher degree; the share of the corresponding eigenface
in the "sum" of the eigenfaces should be greater. So, the reconstructed original image is equal to a sum of all
eigenfaces, with each eigenface having a certain weight and this weight specifies, to what degree the specific
feature (eigenface) is present in the original image .. That is in order to reconstruct the original image from the
eigenfaces, building a kind of weighted sum of all eigenfaces is required. By using all the eigenfaces extracted
from original images, exact reconstruction of the original images is possible. by choosing only the most
important features (eigenfaces) losses due to omitting some of the eigenfaces can be minimized. Suppose there
are C classes in the training data. PCA is based on the sample covariance which characterizes the scatter of the
entire data set, irrespective of class-membership. The projection axes chosen by PCA might not provide good
discrimination power.
The algorithm used for principal component analysis is as follows:
1.1: Mathematics of PCA
A 2-D facial image can be represented as 1-D vector byconcatenating each row (or column) into a
single column(or row) vector.
1) We assume the training sets of images are Γl, Γ2,Γ3...Γm, with each image I(x,y). Where (x, y) is the sizeof the
image. Convert each image into set of vectors andnew full-size matrix (mxp), where m is the number oftraining
images and p is x ×y the size of the image.
2) Find the mean face by:
Ψ=∑I(x,y∕m; L: i =l,…,ri.
3) Calculated the mean-subtracted face:
<1>, = r, - 'P
i = 1,2,3 .... m. and a set of matrix is obtained with
A = [<1>1, <1>2, ...... <l>m,] is the mean-subtracted matrixvector with its size Amp.
4) By implementing the matrix transformations, thevector matrix is reduced by:
Cmm = Amp X ATpm (9)where C is the covariance matrix and T is transposematrix.
5) Find the eigen vectors Vmm and eigen values Amfrom the C matrix and ordered the eigen vectors byhighest
eigen values.
6) Apply the eigen vector matrix, Vmm and adjusted matrix Dm. These vectors determine the
linearcombinations of the training set images to form the eigen faces
7) Instead of using m eigen faces, m'« m which isconsidered as the image provided for training for
eachindividual or m' is the total class used for training.
8) Based on the eigen faces, each image has its facevector by
www.iosrjournals.org 48 | Page
3. Face Recognition Using Gabor features And PCA
Wk = u(r - '1'), k = 1,2, ..., m’.
9) The weights form a feature vector.This feature vectors are taken as the representational basis for the face
images with dimension reduced with m.
IV. RESULTS AND DISCUSSION
In this system is tested with YALE face databaseand its effectiveness is shown in results. The
extractedfeatures is used as it is using the above said featureextraction methods and classification is done using
eigenvaluesand compared with Euclidean distance measure method.The recognition rate is obtained with fixed
number ofPCA features. The performance comparison withEuclidean Distance Measure Classifier (ED) is
shown inTable I.
Tablei. Performance Comparison with Ed Classifier
Euclidean Distance Measure(ED) Classifier
No. of Features PCA GABOR + PCA
40 85.0 89.2
50 82.0 88.5
60 92.5 94.9
70 93.2 98.6
Then using graph, the results analysis can be shown below in figure 2.
Figure 2. Performance comparison with ED classifier
V. CONCLUSION
In this system, a Gabor feature based method used to eliminate the variations due to pose, lighting and
features is used to increase the robustness of the face recognition system. For different scales and orientations of
the Gabor filter, the input image is convoluted with gabor filters.Then this convoluted image feature vectors
were formulated using PCA method. PCA is used to reduce the high dimensionality of these feature vectors.
This discriminating feature space is used as the training feature space for Back propagation Neural Network in
the classification stage. The recognition rate high with features extracted from PCA based Gabor methods than
simple PCA methods. As the number of features selected in PCA increased, this consecutively also increases the
recognition rate. But this in turn increases the computational load.
REFERENCES
[1] Kobonen's system, T. Kobonen, "Self- organization and Associative Memory", Springer-Verlag, Berlin, 1989.
[2] M. Turk and A. Pentland, "Eigenfaces for recognition ", J Cog. Neuroscience, vol. 3, no. I, pp. 71-86, 1991.
[3] Maxim A. Grodin, "Elastic graph matching (EGM) ", On internal representation in face recognition systems, Pattern recognition,
Volume 33, Issue 7, July 2000, Page 1161-1177.
[4] Cooley, W. W. and Lohnes, "PCA correlation", P. R. Multivariate Data Analysis John Wiley & Sons, Inc., New York, 1971).
[5] Thomson, N., Boulgouris, N. V., & Strintzis, M.G. (2006, January)peak signal-to-noise ratio (PSNR),. Optimized Transmission of
JPEG2000 Streams Over Wireless Channels. IEEE Transactions on Image Processing, 15 (I).
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[7] M. Turk and A. Pentland, "Eigenfaces for recognition," J. Cognitive Neuroscience,vol. 3, 71- 86., 1991
[8] Chen, L.F., M.L. Hong-Yuan, K. Ming-Tat, L. JaChen and Y. Gwo-Jong, "A new LDAbased face recognitionsystem which can
solve the small sample size problem", Pattern Recognition, vol 33, pp 17l3-1726, 2000.
[9] Vitomir Struc, Nikola pavesi, "Gabor-Based KernelPartial-Least-Squares Discrimination Features for Face Recognition",
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[10] S. Rajasekaran & G.A. Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms” Prentice-Hall of India Private
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www.iosrjournals.org 49 | Page