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.
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.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
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.
MultiModal Identification System in Monozygotic TwinsCSCJournals
With the increase in the number of twin births in recent decades, there is a need to develop alternate approaches that can secure the biometric system. In this paper an effective fusion scheme is presented that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing Fisher’s Linear Discriminant methods for face matching, Principal Component Analysis for fingerprint matching and Local binary pattern features for iris matching and fused the information for effective recognition and authentication The importance of considering these boundary conditions, such as twins, where the possibility of errors is maximum will lead us to design a more reliable and robust security system.The proposed approach is tested on a real database consisting of 50 pair of identical twin images and shows promising results compared to other techniques. The Receiver Operating Characteristics also shows that the proposed method is superior compared to other techniques under study.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
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.
MultiModal Identification System in Monozygotic TwinsCSCJournals
With the increase in the number of twin births in recent decades, there is a need to develop alternate approaches that can secure the biometric system. In this paper an effective fusion scheme is presented that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing Fisher’s Linear Discriminant methods for face matching, Principal Component Analysis for fingerprint matching and Local binary pattern features for iris matching and fused the information for effective recognition and authentication The importance of considering these boundary conditions, such as twins, where the possibility of errors is maximum will lead us to design a more reliable and robust security system.The proposed approach is tested on a real database consisting of 50 pair of identical twin images and shows promising results compared to other techniques. The Receiver Operating Characteristics also shows that the proposed method is superior compared to other techniques under study.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Efficient Small Template Iris Recognition System Using Wavelet TransformCSCJournals
Iris recognition is known as an inherently reliable biometric technique for human identification. Feature extraction is a crucial step in iris recognition, and the trend nowadays is to reduce the size of the extracted features. Special efforts have been applied in order to obtain low templates size and fast verification algorithms. These efforts are intended to enable a human authentication in small embedded systems, such as an Integrated Circuit smart card. In this paper, an effective eyelids removing method, based on masking the iris, has been applied. Moreover, an efficient iris recognition encoding algorithm has been employed. Different combination of wavelet coefficients which quantized with multiple quantization levels are used and the best wavelet coefficients and quantization levels are determined. The system is based on an empirical analysis of CASIA iris database images. Experimental results show that this algorithm is efficient and gives promising results of False Accept Ratio (FAR) = 0% and False Reject Ratio (FRR) = 1% with a template size of only 364 bits.
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.
Microarray Data Classification Using Support Vector MachineCSCJournals
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
A Review on Geometrical Analysis in Character Recognitioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Performance Comparison of Face Recognition Using DCT Against Face Recognition...CSCJournals
In this paper, a face recognition system using simple Vector quantization (VQ) technique is proposed. Four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to generate codebooks of desired size. Euclidean distance is used as similarity measure to compare the feature vector of test image with that of trainee images. Proposed algorithms are tested on two different databases. One is Georgia Tech Face Database which contains color JPEG images, all are of different size. Another database used for experimental purpose is Indian Face Database. It contains color bitmap images. Using above VQ techniques, codebooks of different size are generated and recognition rate is calculated for each codebook size. This recognition rate is compared with the one obtained by applying DCT on image and LBG-VQ algorithm which is used as benchmark in vector quantization. Results show that KFCG outperforms other three VQ techniques and gives better recognition rate up to 85.4% for Georgia Tech Face Database and 90.66% for Indian Face Database. As no Euclidean distance computations are involved in KMCG and KFCG, they require less time to generate the codebook as compared to LBG and KPE
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
Feature Fusion and Classifier Ensemble Technique for Robust Face RecognitionCSCJournals
Face recognition is an important part of the broader biometric security systems research. In the past, researchers have explored either the Feature Space or the Classifier Space at a time to achieve efficient face recognition. In this work, both the Feature Space optimization as well as the Classifier Space optimization have been used to achieve improved results. The efficient technique of Feature Fusion in the Feature Space and Classifier Ensemble technique in the Classifier Space have been used to achieve robust and efficient face recognition. In the Feature Space, the Discrete Wavelet Transform (DWT) and the Histogram of Oriented Gradient (HOG) features have been extracted from face images and these have been used for classification purposes after Feature Fusion using the Principal Component Analysis (PCA). In the Classifier Space, a Classifier Ensemble has been used, utilizing the bagging technique for ensemble training, instead of a single classifier for efficient classification. Proper selections of various parameters of the DWT, HOG features and the Classification Ensemble have been considered to achieve optimum performance. The proposed classification technique has been applied to the AT&T (ORL) and Yale benchmark face recognition databases, and we have achieved excellent results of 99.78% and 97.72% classification accuracy respectively. The proposed Feature Fusion and Classifier Ensemble technique has been subjected to sensitivity analysis and it has been found to be robust under reduced spatial resolution conditions.
An offline signature recognition and verification system based on neural networkeSAT Journals
Abstract Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit. Keywords: biometrics, neural network design, feature extraction, classification etc.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
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.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Efficient Small Template Iris Recognition System Using Wavelet TransformCSCJournals
Iris recognition is known as an inherently reliable biometric technique for human identification. Feature extraction is a crucial step in iris recognition, and the trend nowadays is to reduce the size of the extracted features. Special efforts have been applied in order to obtain low templates size and fast verification algorithms. These efforts are intended to enable a human authentication in small embedded systems, such as an Integrated Circuit smart card. In this paper, an effective eyelids removing method, based on masking the iris, has been applied. Moreover, an efficient iris recognition encoding algorithm has been employed. Different combination of wavelet coefficients which quantized with multiple quantization levels are used and the best wavelet coefficients and quantization levels are determined. The system is based on an empirical analysis of CASIA iris database images. Experimental results show that this algorithm is efficient and gives promising results of False Accept Ratio (FAR) = 0% and False Reject Ratio (FRR) = 1% with a template size of only 364 bits.
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.
Microarray Data Classification Using Support Vector MachineCSCJournals
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
A Review on Geometrical Analysis in Character Recognitioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Performance Comparison of Face Recognition Using DCT Against Face Recognition...CSCJournals
In this paper, a face recognition system using simple Vector quantization (VQ) technique is proposed. Four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to generate codebooks of desired size. Euclidean distance is used as similarity measure to compare the feature vector of test image with that of trainee images. Proposed algorithms are tested on two different databases. One is Georgia Tech Face Database which contains color JPEG images, all are of different size. Another database used for experimental purpose is Indian Face Database. It contains color bitmap images. Using above VQ techniques, codebooks of different size are generated and recognition rate is calculated for each codebook size. This recognition rate is compared with the one obtained by applying DCT on image and LBG-VQ algorithm which is used as benchmark in vector quantization. Results show that KFCG outperforms other three VQ techniques and gives better recognition rate up to 85.4% for Georgia Tech Face Database and 90.66% for Indian Face Database. As no Euclidean distance computations are involved in KMCG and KFCG, they require less time to generate the codebook as compared to LBG and KPE
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
Feature Fusion and Classifier Ensemble Technique for Robust Face RecognitionCSCJournals
Face recognition is an important part of the broader biometric security systems research. In the past, researchers have explored either the Feature Space or the Classifier Space at a time to achieve efficient face recognition. In this work, both the Feature Space optimization as well as the Classifier Space optimization have been used to achieve improved results. The efficient technique of Feature Fusion in the Feature Space and Classifier Ensemble technique in the Classifier Space have been used to achieve robust and efficient face recognition. In the Feature Space, the Discrete Wavelet Transform (DWT) and the Histogram of Oriented Gradient (HOG) features have been extracted from face images and these have been used for classification purposes after Feature Fusion using the Principal Component Analysis (PCA). In the Classifier Space, a Classifier Ensemble has been used, utilizing the bagging technique for ensemble training, instead of a single classifier for efficient classification. Proper selections of various parameters of the DWT, HOG features and the Classification Ensemble have been considered to achieve optimum performance. The proposed classification technique has been applied to the AT&T (ORL) and Yale benchmark face recognition databases, and we have achieved excellent results of 99.78% and 97.72% classification accuracy respectively. The proposed Feature Fusion and Classifier Ensemble technique has been subjected to sensitivity analysis and it has been found to be robust under reduced spatial resolution conditions.
An offline signature recognition and verification system based on neural networkeSAT Journals
Abstract Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit. Keywords: biometrics, neural network design, feature extraction, classification etc.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
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.
Multi modal face recognition using block based curvelet featuresijcga
In this paper, we present multimodal 2D +3D face recognition method using block based curvelet features. The 3D surface of face (Depth Map) is computed from the stereo face images using stereo vision technique. The statistical measures such as mean, standard deviation, variance and entropy are extracted from each block of curvelet subband for both depth and intensity images independently.In order to compute the decision score, the KNN classifier is employed independently for both intensity and depth map. Further, computed decision scoresof intensity and depth map are combined at decision level to improve the face recognition rate. The combination of intensity and depth map is verified experimentally using benchmark face database. The experimental results show that the proposed multimodal method is better than individual modality.
Technique for recognizing faces using a hybrid of moments and a local binary...IJECEIAES
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delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
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Face Recognition using Improved FFT Based Radon by PSO and PCA TechniquesCSCJournals
Abstract Face Recognition is one of the problems which can be handled very well using a Hybrid technique or mixed transform rather than single technique, it is a very well in terms of a good performance and a large size of the problem. In this paper we represent the using of the Fourier-Based Radon Transform which is improved by the Particle Swarm Optimization (PSO). PSO in this research is used to select the optimum directions (projection angles) that achieve a very high recognition rate and a fast computation. The number of directions selected using PSO is less than the number required by ordinary Radon. This leads to a small number of features. These number of features are reduced farther using PCA to produce a low number of features which used to represent faces in the database. Our method has been applied to ORL Database and achieves 100% recognition rate.
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly
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recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face
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understanding different spoof attacks scenarios and their relation to the developed solutions. A review of
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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
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(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.
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].
3-D Face Recognition Using Improved 3D Mixed TransformCSCJournals
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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
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COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT MATCHING LEVEL
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
DOI : 10.5121/sipij.2017.8404 39
COMPRESSION BASED FACE RECOGNITION
USING TRANSFORM DOMAIN FEATURES
FUSED AT MATCHING LEVEL
Srinivas Halvia
, Nayina Ramapurb
, K B Rajac
and Shanti Prasadd
a
Dayananda Sagar College of Engineering, Bangalore, India.
b
Sai-Tektronix Pvt. Ltd., Bangalore, India.
c
University Visvesvaraya College of Engineering, Bangalore, India.
d
K.S. Institute of Technology, Bangalore, India.
ABSTRACT
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.
KEYWORDS
Biometrics, Face Recognition, FFT, DWT, fusion Technique
1. INTRODUCTION
The biometrics is unique way of identifying humans based on physical and behavioral
characteristics. The biometric is derived from Greek word bio i.e., life and metric i.e., measure,
which is used to measure the human characteristics to authenticate a person. The biometrics is
classified into two group’s viz., physiological and behavioral biometrics. The physiological
biometrics are related to the parts of human body and normally do not vary in the life time. The
example of physiological biometrics are face, fingerprint, DNA, iris, Hand geometry etc. The
behavioral biometrics are related to the behavior of a person and the characteristics are varied
based on mood of a person and circumstances. The examples are signature, gait, key stroke, voice
etc. The biometric system works in two modes viz., (i) Verification mode in which one to one
comparison of biometric features. The captured biometric trait is compared with a specific
biometric trait stored in a biometric database in order to validate a person to be claimed. (ii)
Identification mode in which one to many comparisons of biometrics. The captured biometric trait
is compared with many biometric samples stored in the biometric database in order to establish
the identity of an unknown person. The face is a powerful biometric trait to recognize a person, as
the face image can be captured without any cooperation of humans. The challenges in the face
2. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
40
recognition are pose verifications, illumination variations, expressions, occlusions, age factors,
gender factors etc. The biometrics are used in banks, logical access control, health care, cloud
computing, consumer applications, Border control, Airports, Biometric locks, automobiles, justice
and low enforcement etc.
Contribution: In this paper, Compression based Face Recognition using Transform Domain
Features fused at matching level is proposed. The face images are converted into 1D transform
domains using FFT and DWTs. The performance parameters are computed by comparing features
of database and test face images. The performance parameters are fused at matching level to
obtain better results.
Organization: The remaining section cover literature survey of recent research papers. Section 3
presents proposed method and algorithm is given in Section 4. Section 5 explains performance
analysis of proposed method. The conclusion is given in section 6
2. LITERATURE SURVEY
In this section, the existing techniques of feature extraction based on spatial and transformed
domain methods are explained. The preprocessing techniques to modify original face images and
classifiers to identify face images are also described. Ramy C.G.Chehata, et al.,[1] proposed face
recognition method using transform domain techniques on the MPCA. The techniques are two
dimensional and diagonal modular PCA. The diagonal modular PCA provides more background
information of the face image and it is applied on both overlapping and non-overlapping blocks.
Md. IqbalQuraishi, et al., [2] presented a face recognition method using Artificial Neural
Network. In this method the ripplet transform is used for feature extraction, after preprocess and
image enhancement of the face image.
Jing Wang, et al., [3] have proposed method that uses both the sparsity and correlation.
Previously many papers suggested that the sparsity representation based classification (SRC)
gives the improved performance. The sparsity is useful for selecting the sample and the
correlation is better for comparing the query image and the training samples. Zhao-Rong Lai, et
al., [4] developed a robust method for face recognition using discriminative and compact coding.
In this method two sets of coding models are used, viz., Discriminative Coding (DC) and
Compact Coding (CC). The DC gives the distinguishing decisions and gives the multi-scale error
measurements where as the CC is useful for the face images with different illuminations. The
proposed method combines both DC and CC to perform classification.
Dmitry Gorodnichy and Eric Granger [5] introduced the target based evaluation of face
recognition technology for video surveillance applications. In this two methods are mentioned
Cohert based and Target based methodologies for Watch List Screening (WLS). The proposed
method is evaluated for the choke post public dataset. If the person in the video surveillance
matches with the criminal dataset then the system makes an alarm. Compared to the both above
mentioned techniques the target based method is suitable for the WLS. Yi Jin, et al., [6] proposed
a method for identifying heterogeneous face recognition which contains different types of face
dataset. Coupled Discriminative Feature Learning (CDFL) method is used to recognize the face
for heterogeneous database. In the training session the CDFL seeks to provide most favorable
filters to discriminative face recognition. The subjects with same shape indicates same person.
3. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
41
Asem M. Ali, [7] proposed a method of 3D-Based Pose Invariant Face Recognition at a Distance
Framework for 3D faces recognition. In this method the face image acquisition camera is used.
The captured face image features are extracted and given as input to the second camera, which
will capture the faces of the subject in different orientation. Each face images features are
extracted then 3D reconstruction performed after cropping. The test image is also followed the
same procedure and the bio-signature of test image is obtained. PriyankaDharani and A S Vibhute
[8] proposed a method face recognition technique using wavelet and Neural Network. The DWT
is used to extract features. The Principal Component Analysis is also used along with Fisher
Linear Discriminant to obtain final features. The feed forward neural network is used as classifier
to identify face images. Lihong Wan, et al., [9] proposed face recognition method using
convolutional neural network and subspace learning. The proposed method employs the deep
learning of convolutional neural network. For the dimensionality reduction, two methods are used
namely linear discriminate analysis (LDA) and whitening principal component analysis (WPCA).
The method is tested using two face databases CMU PIE and FERET.
PawanpreetKaur and Deepak Aggarwal [10] proposed a face recognition method using DWT,
LBP and PCA. For the first level feature extraction, DWT and LBP are used separately then PCA
is applied on the fusion of LBP and DWT. For the comparison K-nearest neighbor (KNN)
classifier is used. Kumuda and Basavraj[11] proposed an algorithm to extracting the better
features of text images from complex scene images using Median filtering, Connected
Components, DWT and Morphological operations. Hameed R. Farhan et al, [12] proposed a face
recognition based on discrete HMM which is used to increases the recognition rate in order to
make strong. The concept of novel start with preprocessing to filtering the image with median
filter and DWT. The DWT used to compress the size of input image prepared to generate a
sequence of overlapping blocks. The quantization method is converting each blocks to an integer
value. The 100% recognition rate is achieved with reducing number of stats of HMM.
3. PROPOSED MODEL
In this section compression version face recognition based on transform domain features fused at
matching level is proposed. The proposed face recognition model is shown in the figure 1. The
mean is applied on the face images to convert 2- Dimension (2D) into 1-Dimension (1D) to get
the compressed feature which will provide appropriate features.
The Fast Fourier Transform (FFT) and Discrete Wavelet Transforms (DWT) are applied to obtain
transform domain features. The low frequency coefficients obtained using DWT implies
significant information and the corresponding values are high. The high frequency coefficients of
DWT gives detailed information of the signals and the corresponding values are low ie., may be
zeroes. The log is applied on high frequency coefficients to convert very low values to
moderately high values. The low and converted high frequency values are concatenated to
obtained final DWT features. The features of FFT and DWT are compared separately using ED to
compute Performance Parameters. The fusion formula is used to combine results of FFT and
DWT to obtain better results.
4. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
42
Fig. 1: Block Diagram of Proposed Model
3.1 Face Databases: The face recognition model is tested for various performance parameters
using available face databases such as Olivetti Research Laboratory (ORL), Near Infra Red
(NIR), Yale, Indian Male and Indian Female.
3.1.1) ORL Face database [13]: The Database contains totally 40 subjects. For each subject ten
different orientated face images are captured. The resolution of an image is 112*92. The samples
of one person are shown in figure 2.
Fig. 2. Face images of ORL database
3.1.2) NIR Database [14]: This database created by considering less luminance intensity. The
resolution of image is 576*768. The database consists of 120 persons. Each person has fifteen
face images with different orientations, with and without glasses. The samples of one person is
shown in figure 3.
5. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
43
Fig. 3. Face images of NIR Database
3.1.3) YALE Database [15]: The database consists of 15 persons. Each person has ten different
oriented images with resolution of 240x320. The sample of database image is shown in figure 4.
Fig. 4. Yale Database samples
3.1.4) Indian Male Database [16]: The database consists of twenty persons. Each person has
eleven images with pose and expression variations having a resolution of 640x480. The samples
of database face image are shown in figure 5.
Fig. 5. Indian male database
3.1.5) Indian Female Database [17]: The database consists of twenty two persons with eleven
images per person. The images are captured using a camera with a resolution 640X480. The face
images are captured with different facial expressions and poses. The samples of database are
shown in figure 6.
Fig. 6. Indian female sample images
6. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
44
3.2 1D-Conversion: The 2D image is converted to 1D column vector using mean computation
on rows of image matrix. The advantage of converting 2D to 1D is that the number of pixels in an
image decreases, which results in less computation time and memory requirement. The
illustration of converting 2D matrix into 1D column vector is as shown in figure 7.
Fig. 7 Conversion of 2D matrix to 1D Column Vector
The 8x8 matrix is considered and mean is used on each row to convert row vector into scalar
value. The column vector has every information of 2D matrix in a compressed manner. The
computation time to convert 1D column vector into FFT and DWT domains are fast compared to
2D-FFT and 2D-DWT conversion.
3.3 Fast Fourier Transform Features [18]: The transformation is applied on 1D column
vector to convert time domain into frequency domain. The transform decomposes vector into its
real and imaginary components which is a representation of the vector in the frequency domain.
The FFT of 1D signal given by the equations 1.
= ∑ --------------------------------------------------------------------------------- (1)
Where N is the number of samples.
The computed features are complex variables and the final features considered as absolute values.
3.4 Discrete Wavelet Transform features [19]: This gives information of the frequency
variation along with the localization i.e., the time at which the frequency is varied. The DWT
applied on Signal or 1D array gives two bands L and H i.e., approximate and detail coefficients
respectively. The approximate coefficient has low frequency coefficients represent significant
information similar to input signal or array. The detailed coefficients represent the detailed
information represents high frequency coefficients. The L and H are computed using following
illustration.
The row vector having four elements says A is considered and is converted into DWT bands.
A = 1 2 3 4
The Low (L) and High (H) frequency bands of first two elements of row vector A are computed
using equations 3 and 4 respectively.
=
√!
=
!
√!
= 2.1213------------------------------------------------------------------------------- (3)
7. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
45
# =
√!
=
!
√!
=-0.7071-------------------------------------------------------------------------------- (4)
The low frequency band coefficient values are high compared to high frequency coefficient
values; hence low frequency band has significant information. Similarly L and H bands of third
and fourth elements of A are 4.949747 and -0.7071 respectively. The L and H bands of row vector
A are
L = ( 2.12132 4.949747 ) H = ( -0.7071 -0.7071 )
The very low values of high frequency coefficients are converted into moderate value using Log
(1+H). The final DWT features are obtained by concatenating low and modified high frequency
coefficients.
3.5 Matching Unit: The features of database are compared with test features using Euclidian
Distance (ED) as given in equation 5.
$% = &∑ | () − +) |!,
) ---------------------------------------------------------------------------------- (5)
Where K = Total Number of features
Mi = Feature coefficient values of database vectors
Nii = Feature coefficient values of test vectors
3.6 Matching Fusion: The performance parameters of two methods using FFT and DWT
features separately are fused using normalization fusion technique as given in equation 6.
Fused Opt.TSR=
-./0 12 1034567/.89:45;;8
-./0 12 1034567/.89:45<=8
*(max (opt.TSR) DWT) -------------------------------(6)
4. ALGORITHM
Problem Definition: The face recognition is used to identify a person in several applications. The
face images are identified based on transform domain features and fusion techniques. The
proposed algorithm is given Table 1.
Objectives: The face images are used to recognize persons efficiently and the objectives are
(i) To increase Total Success Rate (TSR) and Optimum TSR
(ii) To decrease Error Rate
(iii) To decrease computation time to extract features.
8. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
46
Table 1: Proposed Face recognition algorithm
5. PERFORMANCE ANALYSIS
In this section, the definitions of performance parameters, result analysis using FFT, DWT and
fusion techniques and comparison of proposed method with existing methods are discussed. The
variations of Performance parameters of each database using different combinations of Person
Inside Database (PID) and Person Outside Database (POD) are also discussed.
5.1 Definitions of Performance Parameters:
1. False Rejection Ratio (FRR): The ratio of the falsely rejected persons in the inside
database to the total number of persons available in the database. The percentage of FRR is
calculated using an equation 7.
>?? =
0@ 3A 45 B3 )03 C3AD4 D A3E3./3F ) /G3 ) D)F3 F / D3
84/ 1 0@ 3A 45 73AD4 D ) /G3 F / D3
------------------------------ (7)
2. False Acceptation Ratio (FAR): The ratio of the persons falsely accepted that are not in
the database to the total number of persons considered from the outside database.
>H? =
0@ 3A 45 C3AD4 D ..37/3F 5A4@ /G3 40/D)F3 F / D3
84/ 1 0@ 3A 45 73AD4 D ) /G3 40/D)F3 F / D3
----------------------------------(8)
3. Total Success Rate (TSR): The ratio of total number of persons matched to the total
number of persons in the database.
IJ? =
0@ 3A 45 C3AD4 D @ /.G3F .4AA3./1K
84/ 1 0@ 3A 45 73AD4 D ) /G3 ) D)F3 F / D3
------------------------------------- (9)
4. Equal Error Rate (EER): The error rate where both the FAR and FRR are equal and less.
5. Half Error Rate (HER): The average of two errors FAR and FRR.
#$? =
;-: ;::
!
---------------------------------------------------------------------(10)
9. Signal & Image Processing : An International Journal (SIPIJ) Vol.8, No.4, August 2017
47
5.2 Analysis of Results using FFT: The performance parameter variations for different face
databases such as Indian Female, Indian Male, NIR, YALE and ORL using FFT technique are
analyzed.
5.2.1 Indian Female Database: The performance parameters are varied with the reference to the
threshold combinations of 7:15, 11:11 and 15:7. The values of percentage FAR and TSR increase
with threshold, where as the FAR values decreased with threshold. The graphical representation
of performance parameters variations with threshold are shown in Figure 8,9 and 10 for PID and
POD combinations of 7:15, 11:11 and 15:7. The percentage values of FAR and TSR are directly
proportional to the threshold, where as the FRR values are inversely proportional to the threshold.
Fig. 8 Variations of FAR, FRR and TSR Fig. 9 Variations of FAR, FRR and
with PID: POD =7:15 TSR with PID: POD =11:11
The percentage EER values are 19, 13 and 15 for PID and POD combinations of 7:15, 11:11 and
15:7 respectively. The percentage opt.TSR values are 80,90 and 85 for PID and POD
combinations of 7:15, 11:11 and 15:7 respectively.
Fig. 10 Variations of FAR, FRR and TSR with PID: POD =15:7
5.2.2 Indian Male Database: The variations of performance parameter values with threshold for
PID and POD combinations of 5:15, 10:10 and 15:5 are shown in figures 11, 12 and 13
respectively. The percentage EER values are for PID: POD combinations of 5:15, 10:10 and 15:5
are 18, 38 and 42 respectively. The percentage opt.TSR values are 80, 61 and 59 for PID and
POD combinations of 5:15, 10:10 and 15:5 respectively.
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Fig. 11 Variations of FAR, FRR and Fig. 12 Variations of FAR, FRR and TSR
TSR with PID: POD =5:15 with PID: POD =10:10
Fig. 13 Variations of FAR, FRR and TSR with PID: POD =15:5
5.2.3 NIR Database: The variations of percentage parameters such as percentage FRR, FAR and
TSR with threshold for PID and POD combinations of 10:30, 20:20 and 30:10 are shown in
Figures 14, 15 and 16.
Fig. 14 Variations of FAR, FRR and TSR Fig. 15 Variations of FAR, FRR and TSR
with PID: POD =10:30 with PID: POD =20:20
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The percentage EER values for PID and POD combinations of 10:30, 20:20 and 30:10 are 28, 48
and 17 respectively. The percentage opt.TSR values are 72, 52 and 80 for PID and POD
combinations of 10:30, 20:20 and 30:10 respectively.
Fig. 16 Variations of FAR, FRR and TSR with PID: POD =30:10
5.2.4 YALE Database: The variations of percentage parameters such as percentage FRR, FAR
and TSR with threshold for PID and POD combinations of 4:11, 8:7 and 11:4 are shown in
Figures 17, 18 and 19.
Fig. 17 Variations of FAR, FRR and Fig. 18 Variations of FAR, FRR and
TSR with PID: POD =4:11 TSR with PID: POD =8:7
The percentage EER values are 28,30 and 38 for PID and POD combinations of 4:11, 8:7 and
11:4 respectively. The corresponding percentage opt.TSR values are 82, 71 and 62.
Fig. 19 Variations of FAR, FRR and TSR with PID: POD =11:4
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5.2.5 ORL Database: The variations of percentage parameters such as percentage FRR, FAR and
TSR with threshold for PID and POD combinations of 10:30, 20:20 and 30:10 are shown in
Figures 20, 21 and 22.
Fig. 20 Variations of FAR, FRR and Fig. 21 Variations of FAR, FRR and
TSR with PID: POD =10:30 TSR with PID: POD =20:20
The Percentage EER values are 18, 21 and 15 for PID and POD combination of 10:30, 20:20 and
30:10. The corresponding percentage opt.TSR values are 82, 69 and 82 respectively.
Fig. 22 Variations of FAR, FRR and TSR with PID: POD =30:10
5.3 Analysis of Results using DWT: The performance parameter variations for different face
databases such as Indian Female, Indian Male, NIR, YALE and ORL using DWT technique are
analyzed.
5.3.1 Indian Female Database: The performance parameters are varied with the reference to the
threshold for various combinations of PID and POD. The percentage variation of FAR,FRR and
TSR for PID and POD combinations of 7:15,11:11 and 15:7 are shown in Figure 23, 24 and 25. It
is observed that the values of FAR and TSR are increases with threshold, whereas FRR decreases
with threshold. The maximum TSR values for PID and POD combinations of 7:15, 11:11 and
15:7 are 85.71, 90.9 and 86.6 respectively. The optimum TSR values are 85.7, 82 and 72for PID
and POD combination of 7:15, 11:11 and 15:7 respectively and the corresponding EER values are
12, 12 and 19.
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Fig. 23 Variations of FAR, FRR and Fig. 24 Variations of FAR, FRR and
TSR with PID: POD =7:15 TSR with PID: POD =11:11
Fig. 25 Variations of FAR, FRR and TSR with PID: POD =15:7
5.3.2 Indian Male Database: The variations of performance parameter values with threshold for
PID and POD combinations of 5:15, 10:10 and 15:5 are shown in figures 26, 27 and 28
respectively. The percentage EER values for PID: POD combinations of 5:15, 10:10 and 15:5 are
20, 39 and 50 respectively and the corresponding percentage optimum TSR values are 80, 60 and
50.
Fig. 26 Variations of FAR, FRR and Fig. 27 Variations of FAR, FRR and
TSR with PID: POD =5:15 TSR with PID: POD =10:10
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Fig. 28 Variations of FAR, FRR and TSR with PID: POD =15:5
5.3.3 NIR Database: The variations of Performance parameters such as percentage FRR, FAR
and TSR with threshold for PID and POD combinations of 10:30, 20:20 and 30:10 are shown in
Figures 29, 30 and 31.
The Percentage optimum TSR values are 72, 62 and 68 for PID and POD combination of 10:30,
20:20 and 30:10 respectively and also the corresponding percentage EER values are 19, 40 and
42.
Fig. 29 Variations of FAR, FRR and Fig. 30 Variations of FAR, FRR and
TSR with PID: POD =10:30 TSR with PID: POD =20:20
Fig. 31 Variations of FAR, FRR and TSR with PID: POD =30:10
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5.3.4 ORL Database: The variations of Performance parameters such as percentage FRR, FAR
and TSR with threshold for PID and POD combinations of 10:30, 20:20 and 30:10 are shown in
Figures 32, 33 and 34. It is observed that the maximum percentage FAR and FRR values are 100
for all combination of PID and POD. The percentage optimum TSR values are 90, 80 and 72 for
PID and POD combinations of 10:30, 20:20 and 30:10 respectively and also the corresponding
percentage EER values are 10, 14 and 22.
Fig. 32 Variations of FAR, FRR and TSR Fig. 33 Variations of FAR, FRR and TSR
with PID: POD =10: 30 with PID: POD =20:20
Fig. 34 Variations of FAR, FRR and TSR with PID: POD =30:10
5.3.5 YALE Database: The variations of performance parameters such as percentage FRR, FAR
and TSR with threshold for PID and POD combinations of 4:11, 8:7 and 11:4 are shown in
Figures 35, 36 and 37. The percentage maximum optimum TSR values are 75, 90 and 80 for PID
and POD combinations of 4:11, 8:7 and 11:4 respectively and also the corresponding percentage
EER values are 25, 10 and 12.
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Fig. 35 Variations of FAR, FRR and Fig. 36 Variations of FAR, FRR and
TSR with PID: POD =4:11 TSR with PID: POD =8:7
Fig. 37. Variations of FAR, FRR and TSR with PID: POD =11:4
5.4 Performance Comparison:
The performance of proposed technique is compared with existing techniques such as FFT and
DWT. The face databases such as ORL, Indian Male, Indian Female and Yale are used with
different combinations of PID and PODs. The performance parameters are compared and are
given in Table 2. The values of performance parameters are based on types of face databases used
to test the algorithm.
The Percentage optimum recognition rate is high with YALE face database, whereas the
recognition rate is low with NIR database. It is observed that, the proposed fusion technique is
better compared to existing techniques in terms of recognition rate and error rate.
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Table 2: Performance comparison of proposed method with existing techniques for ORL, Indian Male,
Indian Female and YALE Face Database
5.5 Performance comparison of proposed method with existing methods:
The performance of the proposed method is compared with existing methods in terms of
percentage optimum TSR (%Opt. TSR) and EER are given in Table 3 for different face
databases.
Table 3: Comparison of proposed method with existing methods
The Indian Male, ORL and Indian Female and ORL face databases are used to compare
performance parameters of proposed method with existing methods presented by Sujatha et al.,
[19, 20, 21]. The performance values of Opt. TSR are high in the case of proposed method
compared to existing methods. The percentage EER values are less in the case of proposed
method compared to existing methods for all face databases. The performance parameters such
as percentage recognition rate and half error rate of proposed method are compared with existing
method presented by Belahcene, et al., [22] using Yale database is given in Table 34. It is
observed that the performance of proposed method is better compared to existing method.
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Table 34: Performance parameters comparison of proposed method with existing methods using Yale
Database
The performance of proposed method is better for the following reasons
i. The 2D face images are converted into 1D vector to reduce number of features without
disturbing much of significant information in the face images to reduce complexity of an
algorithm.
ii. The 1D-FFT and DWT features are better compared to spatial domain features.
iii. The computed performance parameters of FFT and DWT are fused at the matching level
using normalization fusion technique.
iv. The results obtained in the proposed technique support the real time implementation by
increasing speed of computation.
6. CONCUSION
The face is a physiological biometric traits used to recognize a person and used in several
applications.
In this paper, Compression based Face Recognition using Transform Domain Features fused at
Matching Level is proposed. The transform domain techniques such as 1D- FFT and 1D- DWT
are used to extract initial features of face images by converting 2D into 1D. The ED is used to
compare initial features of database images and test images to compute initial performance
parameters. The final performance parameters are obtained by fusing initial performance
parameters at matching level for better results. It is observed that, the performance of the
proposed method is better compared to existing methods. In future, the proposed method will be
implemented using hardware for real time applications.
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