The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
Ā
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVMsipij
Ā
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 (SVās). The LL coefficients of DWT and SVās are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
Face Detection in Digital Image: A Technical ReviewIJERA Editor
Ā
Face detection is the method of focusing faces in input image is an important part of any face processing system. In Face detection, segmentation plays the major role to detect the face. There are many contests for effective and efficient face detection. The aim of this paper is to present a review on several algorithms and methods used for face detection. We read the various surveys and related various techniques according to how they extract features and what learning algorithms are adopted for. Face detection system has two major phases, first to segment skin region from an image and second to decide these regions cover human face or not. There are number of algorithms used in face detection namely Genetic, Hausdorff Distance etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Filtering of Frequency Components for Privacy Preserving Facial RecognitionArtur Filipowicz
Ā
This paper examines the use of signal processing and feature engineering techniques to design a facial recognition system with image-reconstruction privacy protection. The Fast Fourier Transform (FFT) and Wavelet Transform (WT) are used to derive features from face images in the Yale and Olivetti datasets. Then, the features are selected by a filter. We propose several filters that fall into three categories ā conventional filters (rectangular and triangular), unsupervised-learning filter (variance), and supervised-learning filter (SNR, FDR, SD, and t-test). Furthermore, we investigate the role of FFT phase removal as a possible tool for image reconstruction privacy protection. The results show that both filtering and FFT phase removal can prevent privacy-compromising reconstruction of the original images without sacrificing recognition accuracy. Among the filters, we found the SNR and t-test filters to yield the best recognition accuracies while preserving the image-reconstruction privacy. This work presents a great promise for signal processing and feature engineering as a tool toward building privacy-preserving facial recognition systems.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
Ā
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVMsipij
Ā
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 (SVās). The LL coefficients of DWT and SVās are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
Face Detection in Digital Image: A Technical ReviewIJERA Editor
Ā
Face detection is the method of focusing faces in input image is an important part of any face processing system. In Face detection, segmentation plays the major role to detect the face. There are many contests for effective and efficient face detection. The aim of this paper is to present a review on several algorithms and methods used for face detection. We read the various surveys and related various techniques according to how they extract features and what learning algorithms are adopted for. Face detection system has two major phases, first to segment skin region from an image and second to decide these regions cover human face or not. There are number of algorithms used in face detection namely Genetic, Hausdorff Distance etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Filtering of Frequency Components for Privacy Preserving Facial RecognitionArtur Filipowicz
Ā
This paper examines the use of signal processing and feature engineering techniques to design a facial recognition system with image-reconstruction privacy protection. The Fast Fourier Transform (FFT) and Wavelet Transform (WT) are used to derive features from face images in the Yale and Olivetti datasets. Then, the features are selected by a filter. We propose several filters that fall into three categories ā conventional filters (rectangular and triangular), unsupervised-learning filter (variance), and supervised-learning filter (SNR, FDR, SD, and t-test). Furthermore, we investigate the role of FFT phase removal as a possible tool for image reconstruction privacy protection. The results show that both filtering and FFT phase removal can prevent privacy-compromising reconstruction of the original images without sacrificing recognition accuracy. Among the filters, we found the SNR and t-test filters to yield the best recognition accuracies while preserving the image-reconstruction privacy. This work presents a great promise for signal processing and feature engineering as a tool toward building privacy-preserving facial recognition systems.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
Ā
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
Literature Survey on Image Deblurring TechniquesEditor IJCATR
Ā
Image restoration and recognition has been of great importance nowadays. Face recognition becomes difficult when it comes
to blurred and poorly illuminated images and it is here face recognition and restoration come to picture. There have been many
methods that were proposed in this regard and in this paper we will examine different methods and technologies discussed so far. The
merits and demerits of different methods are discussed in this concern
Target Detection Using Multi Resolution Analysis for Camouflaged Images ijcisjournal
Ā
Target detection is a challenging problem having many applications in defense and civil. Most of the
targets in defense are camouflaged. It is difficult for a system to detect camouflaged targets in an image. A
novel and constructive approach is proposing to detect object in camouflage images. This method uses
various methodologies such as 2-D DWT, gray level co-occurrence matrix (GLCM), wavelet coefficient
features, region growing algorithm and canny edge detection. Target detection is achieved by calculating
wavelet coefficient features from GLCM of transformed sub blocks of the image. Seed block is obtained by
evaluating wavelet coefficient features. Finally the camouflage object is highlighted using image
processing schemes. The proposed target detection system is implemented in Matlab 7.7.0 and tested on
different kinds of images.
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Ā
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient humanācomputer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
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.
Image deblurring based on spectral measures of whitenessijma
Ā
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
IRJET-Analysis of Face Recognition System for Different ClassifierIRJET Journal
Ā
M.Manimozhi, A. John Dhanaseely "Analysis of Face Recognition System for Different Classifier ", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net .published by Fast Track Publications
Abstract
Face recognition plays vital role for authenticating system. Human Face recognition is a challenging task in computer vision and pattern recognition. Face recognition has attracted much attention due to its potential value in security and law enforcement applications and its theoretical challenges. Different methods are used for feature extraction and classification. Kernel fisher analysis is used for feature extraction. The performance analysis for Euclidean, support vector machine is evaluated. The whole process is done using MATLAB software. A set of 10 person real time images is taken for our work. The classifier recognizes the similar posture as an output.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
Ā
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
Frequency Domain Blockiness and Blurriness Meter for Image Quality AssessmentCSCJournals
Ā
Image and video compression introduces distortions (artefacts) to the coded image. The most prominent artefacts added are blockiness and blurriness. Many existing quality meters are normally distortion-specific. This paper proposes an objective quality meter for quantifying the combined blockiness and blurriness distortions in frequency domain. The model first applies edge detection and cancellation, then spatial masking to mimic the characteristics of the human visual system. Blockiness is then estimated by transforming image into frequency domain, followed by finding the ratio of harmonics to other AC components. Blurriness is determined by comparing the high frequency coefficients of the reference and coded images due to the fact that blurriness reduces the high frequency coefficients. Then, both blockiness and blurriness distortions are combined for a single quality metric. The meter is tested on blocky and blurred images from the LIVE image database, with a correlation coefficient of 95-96%.
An offline signature recognition and verification system based on neural networkeSAT Journals
Ā
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.
The paper presents a methodology for detecting a virtual passive pointer. The passive pointer or device does not have any active energy source within it (as opposed to a laser pointer) and thus cannot easily be detected or identified. The modeling and simulation task is carried out by generating high resolution color images of a pointer viewing via two digital cameras with a popular three-dimensional (3D) computer graphics and animation program, Studio 3D Max by Discreet. These images are then retrieved for analysis into a Microsoftās Visual C++ program developed based on the theory of image triangulation. The program outputs a precise coordinates of the pointer in the 3D space in addition to itās projection on a view screen located in a large display/presentation room. The computational results of the pointer projection are compared with the known locations specified by the Studio 3D Max for different simulated configurations. High pointing accuracy is achieved: a pointer kept 30 feet away correctly hits the target location within a few inches. Thus this technology can be used in presenter-audience applications.
SELF-LEARNING AI FRAMEWORK FOR SKIN LESION IMAGE SEGMENTATION AND CLASSIFICATIONijcsit
Ā
Image segmentation and classification are the two main fundamental steps in pattern recognition. To perform medical image segmentation or classification with deep learning models, it requires training on large image dataset with annotation. The dermoscopy images (ISIC archive) considered for this work does not have ground truth information for lesion segmentation. Performing manual labelling on this dataset is time-consuming. To overcome this issue, self-learning annotation scheme was proposed in the two-stage deep learning algorithm. The two-stage deep learning algorithm consists of U-Net segmentation model with the annotation scheme and CNN classifier model. The annotation scheme uses a K-means clustering algorithm along with merging conditions to achieve initial labelling information for training the U-Net model. The classifier models namely ResNet-50 and LeNet-5 were trained and tested on the image dataset without segmentation for comparison and with the U-Net segmentation for implementing the proposed self-learning Artificial Intelligence (AI) framework. The classification results of the proposed AI framework achieved training accuracy of 93.8% and testing accuracy of 82.42% when compared with the two classifier models directly trained on the input images.
OFFLINE SIGNATURE RECOGNITION VIA CONVOLUTIONAL NEURAL NETWORK AND MULTIPLE C...IJNSA Journal
Ā
One of the most important processes used by companies to safeguard the security of information and prevent it from unauthorized access or penetration is the signature process. As businesses and individuals move into the digital age, a computerized system that can discern between genuine and faked signatures is crucial for protecting people's authorization and determining what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and KNN (k-nearest neighbors), these algorithms are compared to calculate the run time, classification error, classification loss, and accuracy for test-set consist of signature images (genuine and forgery). Three classifiers have been applied using (UTSig) dataset; where run time, classification error, classification loss and accuracy were calculated for each classifier in the verification phase, the results showed that the SVM and KNN got the best accuracy (76.21), while the SVM got the best run time (0.13) result among other classifiers, therefore the SVM classifier got the best result among the other classifiers in terms of our measures.
Hand gesture recognition using support vector machinetheijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
Ā
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.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Determination of the Cost of Production from the Raw Dung to the Final Outpu...theijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
Ā
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
Literature Survey on Image Deblurring TechniquesEditor IJCATR
Ā
Image restoration and recognition has been of great importance nowadays. Face recognition becomes difficult when it comes
to blurred and poorly illuminated images and it is here face recognition and restoration come to picture. There have been many
methods that were proposed in this regard and in this paper we will examine different methods and technologies discussed so far. The
merits and demerits of different methods are discussed in this concern
Target Detection Using Multi Resolution Analysis for Camouflaged Images ijcisjournal
Ā
Target detection is a challenging problem having many applications in defense and civil. Most of the
targets in defense are camouflaged. It is difficult for a system to detect camouflaged targets in an image. A
novel and constructive approach is proposing to detect object in camouflage images. This method uses
various methodologies such as 2-D DWT, gray level co-occurrence matrix (GLCM), wavelet coefficient
features, region growing algorithm and canny edge detection. Target detection is achieved by calculating
wavelet coefficient features from GLCM of transformed sub blocks of the image. Seed block is obtained by
evaluating wavelet coefficient features. Finally the camouflage object is highlighted using image
processing schemes. The proposed target detection system is implemented in Matlab 7.7.0 and tested on
different kinds of images.
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Ā
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient humanācomputer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
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.
Image deblurring based on spectral measures of whitenessijma
Ā
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
IRJET-Analysis of Face Recognition System for Different ClassifierIRJET Journal
Ā
M.Manimozhi, A. John Dhanaseely "Analysis of Face Recognition System for Different Classifier ", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net .published by Fast Track Publications
Abstract
Face recognition plays vital role for authenticating system. Human Face recognition is a challenging task in computer vision and pattern recognition. Face recognition has attracted much attention due to its potential value in security and law enforcement applications and its theoretical challenges. Different methods are used for feature extraction and classification. Kernel fisher analysis is used for feature extraction. The performance analysis for Euclidean, support vector machine is evaluated. The whole process is done using MATLAB software. A set of 10 person real time images is taken for our work. The classifier recognizes the similar posture as an output.
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
Ā
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
Frequency Domain Blockiness and Blurriness Meter for Image Quality AssessmentCSCJournals
Ā
Image and video compression introduces distortions (artefacts) to the coded image. The most prominent artefacts added are blockiness and blurriness. Many existing quality meters are normally distortion-specific. This paper proposes an objective quality meter for quantifying the combined blockiness and blurriness distortions in frequency domain. The model first applies edge detection and cancellation, then spatial masking to mimic the characteristics of the human visual system. Blockiness is then estimated by transforming image into frequency domain, followed by finding the ratio of harmonics to other AC components. Blurriness is determined by comparing the high frequency coefficients of the reference and coded images due to the fact that blurriness reduces the high frequency coefficients. Then, both blockiness and blurriness distortions are combined for a single quality metric. The meter is tested on blocky and blurred images from the LIVE image database, with a correlation coefficient of 95-96%.
An offline signature recognition and verification system based on neural networkeSAT Journals
Ā
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.
The paper presents a methodology for detecting a virtual passive pointer. The passive pointer or device does not have any active energy source within it (as opposed to a laser pointer) and thus cannot easily be detected or identified. The modeling and simulation task is carried out by generating high resolution color images of a pointer viewing via two digital cameras with a popular three-dimensional (3D) computer graphics and animation program, Studio 3D Max by Discreet. These images are then retrieved for analysis into a Microsoftās Visual C++ program developed based on the theory of image triangulation. The program outputs a precise coordinates of the pointer in the 3D space in addition to itās projection on a view screen located in a large display/presentation room. The computational results of the pointer projection are compared with the known locations specified by the Studio 3D Max for different simulated configurations. High pointing accuracy is achieved: a pointer kept 30 feet away correctly hits the target location within a few inches. Thus this technology can be used in presenter-audience applications.
SELF-LEARNING AI FRAMEWORK FOR SKIN LESION IMAGE SEGMENTATION AND CLASSIFICATIONijcsit
Ā
Image segmentation and classification are the two main fundamental steps in pattern recognition. To perform medical image segmentation or classification with deep learning models, it requires training on large image dataset with annotation. The dermoscopy images (ISIC archive) considered for this work does not have ground truth information for lesion segmentation. Performing manual labelling on this dataset is time-consuming. To overcome this issue, self-learning annotation scheme was proposed in the two-stage deep learning algorithm. The two-stage deep learning algorithm consists of U-Net segmentation model with the annotation scheme and CNN classifier model. The annotation scheme uses a K-means clustering algorithm along with merging conditions to achieve initial labelling information for training the U-Net model. The classifier models namely ResNet-50 and LeNet-5 were trained and tested on the image dataset without segmentation for comparison and with the U-Net segmentation for implementing the proposed self-learning Artificial Intelligence (AI) framework. The classification results of the proposed AI framework achieved training accuracy of 93.8% and testing accuracy of 82.42% when compared with the two classifier models directly trained on the input images.
OFFLINE SIGNATURE RECOGNITION VIA CONVOLUTIONAL NEURAL NETWORK AND MULTIPLE C...IJNSA Journal
Ā
One of the most important processes used by companies to safeguard the security of information and prevent it from unauthorized access or penetration is the signature process. As businesses and individuals move into the digital age, a computerized system that can discern between genuine and faked signatures is crucial for protecting people's authorization and determining what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and KNN (k-nearest neighbors), these algorithms are compared to calculate the run time, classification error, classification loss, and accuracy for test-set consist of signature images (genuine and forgery). Three classifiers have been applied using (UTSig) dataset; where run time, classification error, classification loss and accuracy were calculated for each classifier in the verification phase, the results showed that the SVM and KNN got the best accuracy (76.21), while the SVM got the best run time (0.13) result among other classifiers, therefore the SVM classifier got the best result among the other classifiers in terms of our measures.
Hand gesture recognition using support vector machinetheijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
Ā
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.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Determination of the Cost of Production from the Raw Dung to the Final Outpu...theijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Development of a power factor model for power sysytem loadstheijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Using the Physicochemical Properties and the Thermo-oxidation Degradation Pro...theijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Implementing a Robust Network-Based Intrusion Detection Systemtheijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering and Science (IJES)theijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Developing Accident Avoidance Program for Occupational Safety and Healththeijes
Ā
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A hybrid approach for face recognition using a convolutional neural network c...IAESIJAI
Ā
Facial recognition technology has been used in many fields such as security,
biometric identification, robotics, video surveillance, health, and commerce
due to its ease of implementation and minimal data processing time.
However, this technology is influenced by the presence of variations such as
pose, lighting, or occlusion. In this paper, we propose a new approach to
improve the accuracy rate of face recognition in the presence of variation or
occlusion, by combining feature extraction with a histogram of oriented
gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the
Canny contour detector techniques, as well as a convolutional neural
network (CNN) architecture, tested with several combinations of the
activation function used (Softmax and SegmoĆÆd) and the optimization
algorithm used during training (adam, Adamax, RMSprop, and stochastic
gradient descent (SGD)). For this, a preprocessing was performed on two
databases of our database of faces (ORL) and Sheffield faces used, then we
perform a feature extraction operation with the mentioned techniques and
then pass them to our used CNN architecture. The results of our simulations
show a high performance of the SIFT+CNN combination, in the case of the
presence of variations with an accuracy rate up to 100%.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
Possibility fuzzy c means clustering for expression invariant face recognitionIJCI JOURNAL
Ā
Face being the most natural method of identification for humans is one of the most significant biometric
modalities and various methods to achieve efficient face recognition have been proposed. However the
changes in face owing to different expressions, pose, makeup, illumination, age bring about marked
variations in the facial image. These changes will inevitably occur and they can be controlled only till a
certain degree beyond which they are bound to happen and will affect the face thereby adversely impacting
the performance of any face recognition system. This paper proposes a strategy to improve the
classification methodology in face recognition by using Possibility Fuzzy C-Means Clustering (PFCM).
This clustering technique was used for face recognition due to its properties like outlier insensitivity which
make it a suitable candidate for use in designing such robust applications.PFCM is a hybridization of
Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) clustering algorithms. PFCM is a robust
clustering technique and is especially significant for its noise insensitivity. It has also resolved the
coincident clusters problem which is faced by other clustering techniques. Therefore the technique can also
be used to increase the overall robustness of a face recognition system and thereby increase its invariance
and make it a reliably usable biometric modality.
EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Fa...IJECEIAES
Ā
This paper presents a new technique called Entropy based SIFT (EV-SIFT) for accurate face recognition after the plastic surgery. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the entropy is the higher order statistical feature. The corresponding EV-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the V- SIFT feature extracts the key points based on the volume of the structure. However, the EV- SIFT method provides both the contrast and volume information. Thus EV-SIFT provide better performance when compared with PCA, normal SIFT and VSIFT based feature extraction.
Multimodal authentication is one of the prime concepts in current applications of real scenario. Various
approaches have been proposed in this aspect. In this paper, an intuitive strategy is proposed as a
framework for providing more secure key in biometric security aspect. Initially the features will be
extracted through PCA by SVD from the chosen biometric patterns, then using LU factorization technique
key components will be extracted, then selected with different key sizes and then combined the selected key
components using convolution kernel method (Exponential Kronecker Product - eKP) as Context-Sensitive
Exponent Associative Memory model (CSEAM). In the similar way, the verification process will be done
and then verified with the measure MSE. This model would give better outcome when compared with SVD
factorization[1] as feature selection. The process will be computed for different key sizes and the results
will be presented.
A face recognition system using convolutional feature extraction with linear...IJECEIAES
Ā
Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).
Facial recognition based on enhanced neural networkIAESIJAI
Ā
Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset.
Performance Comparison of Face Recognition Using DCT Against Face Recognition...CSCJournals
Ā
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
K-MEDOIDS CLUSTERING USING PARTITIONING AROUND MEDOIDS FOR PERFORMING FACE R...ijscmc
Ā
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality
Draft activity recognition from accelerometer data
Ā
The International Journal of Engineering and Science (IJES)
1. The International Journal of Engineering And Science (IJES)
||Volume|| 1 ||Issue|| 2 ||Pages|| 215-220 ||2012||
ISSN: 2319 ā 1813 ISBN: 2319 ā 1805
Face Recognition using DCT - DWT Interleaved Coefficient
Vectors with NN and SVM Classifier
1
Divya Nayak (M. Tech.) 1Mr. Sumit Sharma
1, 2
Shri Ram Institute of Technology Jabalpur (M.P.)
---------------------------------------------------------------Abstract-------------------------------------------------------------
Face recognition applications are continuously gaining demands because of their requirements person
authentication, access control and surveillance systems. The researchers are continuously working for making
the system more accurate and faster in the part of that research this paper presents a face recognition system
which uses DWT, DCT interleaved component for feature vector formation & tested the Support Vector
Machine and ANN for classification. Finally the proposed technique is implemented and comprehensively
analyzes to test its efficiency we also compared these two classification method.
Key Words: Face Recognition, Support Vector Machine (SVM), Artificial Neural Network (ANN).
----------------------------------------------------------------------------------------------------------------------------- ----------
Date of Submission: 06, December, 2012 Date of Publication: 25, December 2012
---------------------------------------------------------------------------------------------------------------------------------------
I Introduction II Related Work
This section presents some of the most
Increasing security demands are forcing the relevant work recent work presented by other
scientists and researchers to develop more researchers. Ignas Kukenys and Brendan McCane
advanced security systems one of them is biometric [2] describe a component-based face detector using
security system this system is particularly preferred support vector machine classifiers. They present
because of its proven natural uniqueness and user current results and outline plans for future work
has no need to carry additional devices like cares, required to achieve sufficient speed and accuracy to
remote etc. the biometric security systems refers to use SVM classifiers in an online face recognition
the identification of humans by their characteristics system. They used a straightforward approach in
or traits. Biometrics is used in computer science as implementing SVM classifier with a Gaussian
a form of identification and access control [1]. It is kernel that detects eyes in grayscale images, a first
also used to identify individuals in groups that are step towards a component-based face detector.
under surveillance. One of the biometric Details on design of an iterative bootstrapping
identification is done by the face of the person, this process are provided, and we show which training
method has several application from online parameter values tend to give best results. Jixiong
(person surveillance) to offline (scanned image Wang (jameswang) [3] presented a detailed report
identification) etc. face recognition system has its on using support vector machine and application of
own advantage over other biometric methods that it different kernel functions (Linear kernel,
can be detected from much more distance without Polynomial kernel, Radial basis kernel, Sigmoid
need of special sensors or scanning devices. kernel) and multiclass classification methods and
parameter optimization. Face recognition in the
There are several methods are proposed so presence of pose changes remains a largely
far for the face recognition system using different unsolved problem. Severe pose changes, resulting
feature extraction techniques or different training in dramatically different appearances, are one of
approaches or different classification approaches to the main difficulties and one solution approach is
improve the efficiency of the system. The rest of presented by Antony Lam and Christian R. Shelton
the paper is arrange as the second section presents a [4] they present a support vector machine (SVM)
short review of the work done so far, the third based system that learns the relations between
section presents the details of technical terms used corresponding local regions of the face in different
in the algorithm, the fourth section presents poses as well as a simple SVM based system for
proposed algorithm followed by analysis an automatic alignment of faces in differing poses.
conclusion in next chapters. Jennifer Huang, Volker Blanz and Bernd Heisele
[5] present an approach to pose and illumination
invariant face recognition that combines two recent
advances in the computer vision field: component-
www.theijes.com The IJES Page 215
2. Face Recognition using DCT - DWT Interleaved Coefficient Vectors With NN and SVM Classifier
based recognition and 3D morph able models. In passed through a low pass filter with impulse
preliminary experiments we show the potential of response g resulting in a convolution of the two:
our approach regarding pose and illumination
invariance. A Global versus Component based
Approach for Face Recognition with Support
Vector Machines is presented by Bernd Heisele, The signal is also decomposed simultaneously
Purdy Ho, Tomaso Poggio [6]. They present a using a high-pass filter h. The output gives the
component based method and two global methods detail coefficients (from the high-pass filter) and
for face recognition and evaluate them with respect approximation coefficients (from the low-pass). It
to robustness against pose changes. In the is important that the two filters are related to each
component system they first locate facial other and they are known as a quadrature mirror
components, extract them and combine them into a filter. However, since half the frequencies of the
single feature vector which is classified by a signal have now been removed, half the samples
Support Vector Machine (SVM). The two global can be discarded according to Nyquistās rule. The
systems recognize faces by classifying a single filter outputs are then sub sampled by 2 (Mallat's
feature vector consisting of the gray values of the and the common notation is the opposite, g- high
whole face image. The component system clearly pass and h- low pass):
outperformed both global systems on all tests.
Yongmin Li, Shaogang Gong, Jamie Sherrah,
Heather Liddell [7] presented face detection across
multiple views (frontal view, owing to the
signiļ¬cant non-linear variation caused by rotation
in depth, self-occlusion and self-shadowing). In
their approach the view sphere is separated into
several small segments. On each segment, a face This decomposition has halved the time resolution
detector is constructed. They explicitly estimate the since only half of each filter output characterizes
pose of an image regardless of whether or not it is a the signal. However, each output has half the
face. A pose estimator is constructed using Support frequency band of the input so the frequency
Vector Regression. The pose information is used to resolution has been doubled.
choose the appropriate face detector to determine if
it is a face. With this pose-estimation based The DWT and IDWT for an one-dimensional
method, considerable computational efficiency is signal can be also described in the form of two
achieved. Meanwhile, the detection accuracy is also channel tree-structured filter banks. The DWT
improved since each detector is constructed on a and IDWT for a two-dimensional image x[m,n] can
small range of views. Rectangle Features based be similarly defined by implementing DWT and
method is presented by Qiong Wang, Jingyu Yang, IDWT for each dimension m and n separately
and Wankou Yang [8] presents an efficient DWT n [DWT m [x[m,n]], which is shown in
approach to achieve accurate face detection in still Figure 1.
gray level images. Characteristics of intensity and
symmetry in eye region are used as robust cues to
find possible eye pairs. Three rectangle features are
developed to measure the intensity relations and
symmetry. According to the eye-pair-like regions
which have been found, the corresponding
square image patches are considered to be face
candidates, and then all the candidates are
verified by using SVM. Finally, all the faces in
the image are detected.
Figure 1: DWT for two-dimensional images [9]
III Discrete Wavelet Transform (Dwt)
In numerical analysis and functional An image can be decomposed into a
analysis, a discrete wavelet transform (DWT) is pyramidal structure, which is shown in Figure 2,
any wavelet transform for which the wavelets are with various band information: low-low frequency
discretely sampled. As with other wavelet band LL, low-high frequency band LH, high-low
transforms, a key advantage it has over Fourier frequency band HL, high-high frequency band
transforms is temporal resolution: it captures both HH.
frequency and location information (location in
time).
The DWT of a signal x is calculated by passing it
through a series of filters. First the samples are
www.theijes.com The IJES Page 216
3. Face Recognition using DCT - DWT Interleaved Coefficient Vectors With NN and SVM Classifier
nonlinearly projecting the training data in the input
space to a feature space of higher dimension by use
of a kernel function. This results in a linearly
separable dataset that can be separated by a linear
classifier. This process enables the classification of
datasets which are usually nonlinearly separable in
the input space. The functions used to project the
data from input space to feature space are called
kernels (or kernel machines), examples of which
include polynomial, Gaussian (more commonly
referred to as radial basis functions) and quadratic
functions. By their nature SVMs are intrinsically
binary classifiers however there are strategies by
Figure 2: Pyramidal structure which they can be adapted to multiclass tasks. But
in our case we not need multiclass classification.
IV Discrete Cosine Transform (DCT)
A discrete cosine transform (DCT) 5.1 SVM classification
expresses a sequence of finitely many data points Let xiā Rm be a feature vector or a set of
in terms of a sum of cosine functions oscillating at input variables and let yiā {+1, ā1} be a
different frequencies. DCTs are important to corresponding class label, where m is the
numerous applications in science and engineering, dimension of the feature vector. In linearly
from lossy compression of audio (e.g. MP3) and separable cases a separating hyper-plane satisfies
images (e.g. JPEG) (where small high-frequency [8].
components can be discarded), to spectral methods
for the numerical solution of partial differential
equations.
Formally, the discrete cosine transform is
a linear, invertible function f: RN ā RN (where R
denotes the set of real numbers), or equivalently an
invertible N Ć N square matrix. There are several
variants of the DCT with slightly modified
definitions. The N real numbers x0... xN-1 are
transformed into the N real numbers X0... XN-
1 according to one of the formulas:
The 2D DCT is given by
Figure 3: Maximum-margin hyper-plane and
margins for an SVM trained with samples from two
classes. Samples on the margin are called the
support vectors.
Where the hyper-plane is denoted by a vector of
weights w and a bias term b. The optimal
5. Support Vector Machine (SVM)
separating hyper-plane, when classes have equal
Support Vector Machines (SVMs) have developed
loss-functions, maximizes the margin between the
from Statistical Learning Theory [6]. They have
hyper-plane and the closest samples of classes. The
been widely applied to fields such as character,
margin is given by
handwriting digit and text recognition, and more
recently to satellite image classification. SVMs,
like ANN and other nonparametric classifiers have
a reputation for being robust. SVMs function by
www.theijes.com The IJES Page 217
4. Face Recognition using DCT - DWT Interleaved Coefficient Vectors With NN and SVM Classifier
the solution of the Lagrangian, all data points with
The optimal separating hyper-plane can now be nonzero (and nonnegative) Lagrange multipliers
solved by maximizing (2) subject to (1). The are called support vectors (SV).
solution can be found using the method of Often the hyperplane that separates the training
Lagrange multipliers. The objective is now to data perfectly would be very complex and would
minimize the Lagrangian not generalize well to external data since data
generally includes some noise and outliers.
Therefore, we should allow some violation in (1)
and (6). This is done with the nonnegative slack
variable Ī¶i
and requires that the partial derivatives of w and b
be zero. In (3), Ī±i is nonnegative Lagrange
multipliers. Partial derivatives propagate to The slack variable is adjusted by the regularization
constraints . constant C, which determines the tradeoff between
Substituting w into (3) gives the dual form complexity and the generalization properties of the
classifier. This limits the Lagrange multipliers in
the dual objective function (5) to the range 0 ā¤ Ī±i ā¤
C. Any function that is derived from mappings to
the feature space satisfies the conditions for the
which is not anymore an explicit function of w or kernel function.
b. The optimal hyper-plane can be found by
maximizing (4) subject to and all The choice of a Kernel depends on the problem at
Lagrange multipliers are nonnegative. However, in hand because it depends on what we are trying to
most real world situations classes are not linearly model.
separable and it is not possible to find a linear
hyperplane that would satisfy (1) for all i = 1. . . n. The SVM gives the following advantages over
In these cases a classification problem can be made neural networks or other AI methods (link for more
linearly separable by using a nonlinear mapping details http://www.svms.org).
into the feature space where classes are linearly
SVM training always finds a global minimum, and
separable. The condition for perfect classification
their simple geometric interpretation provides
can now be written as
fertile ground for further investigation.
Most often Gaussian kernels are used, when the
resulted SVM corresponds to an RBF network with
Gaussian radial basis functions. As the SVM
where Ī¦ is the mapping into the feature space. approach āautomaticallyā solves the network
Note that the feature mapping may change the complexity problem, the size of the hidden layer is
dimension of the feature vector. The problem now obtained as the result of the QP procedure. Hidden
is how to find a suitable mapping Ī¦ to the space neurons and support vectors correspond to each
where classes are linearly separable. It turns out other, so the center problems of the RBF network is
that it is not required to know the mapping also solved, as the support vectors serve as the
explicitly as can be seen by writing (5) in the dual basis function centers.
form
Classical learning systems like neural networks
suffer from their theoretical weakness, e.g. back-
propagation usually converges only to locally
optimal solutions. Here SVMs can provide a
significant improvement.
and replacing the inner product in (6) with a
suitable kernel function . The absence of local minima from the above
algorithms marks a major departure from
This form arises from the same procedure as was traditional systems such as neural networks.
done in the linearly separable case that is, writing
the Lagrangian of (6), solving partial derivatives, SVMs have been developed in the reverse order to
and substituting them back into the Lagrangian. the development of neural networks (NNs). SVMs
Using a kernel trick, we can remove the explicit evolved from the sound theory to the
calculation of the mapping Ī¦ and need to only implementation and experiments, while the NNs
solve the Lagrangian (5) in dual form, where the followed more heuristic path, from applications and
inner product has been transposed with the extensive experimentation to the theory.
kernel function in nonlinearly separable cases. In
www.theijes.com The IJES Page 218
5. Face Recognition using DCT - DWT Interleaved Coefficient Vectors With NN and SVM Classifier
Face No. TP TN FP FN Accuracy Precision Recall F-Meas.
V Proposed Algorithm 1 0.6 0.9889 0.0111 0.4 0.95 0.8571 0.6 0.7059
The proposed algorithm can be described 2 0.8 1 0 0.2 0.98 1 0.8 0.8889
3 1 0.9889 0.0111 0 0.99 0.9091 1 0.9524
in following steps. 4 1 0.9889 0.0111 0 0.99 0.9091 1 0.9524
5 0.9 0.9667 0.0333 0.1 0.96 0.75 0.9 0.8182
1. Firstly divide the image into 8x8 blocks then 6 1 0.9444 0.0556 0 0.95 0.6667 1 0.8
take the 2D DCT of the image & then select the 7
8
1
0.9
0.9778
1
0.0222
0
0
0.1
0.98
0.99
0.8333
1
1
0.9
0.9091
0.9474
components in zigzag manner. 9 0.8 0.9889 0.0111 0.2 0.97 0.8889 0.8 0.8421
10 0.8 0.9889 0.0111 0.2 0.97 0.8889 0.8 0.8421
2. Secondly we take the 2D DWT of the image
Average 0.88 0.9833 0.0167 0.12 0.973 0.8703 0.88 0.8658
blocks & then select the zigzag components of only
LL components. Table 2: Result for 40 Faces and 10 Samples
Each using NN
3. Now the feature vectors are formed by Face No. TP TN FP FN Accuracy Precision Recall F-Meas.
1 0.9 0.9889 0.0111 0.1 0.98 0.9 0.9 0.9
interleaving these two components (even 2 0.8 1 0 0.2 0.98 1 0.8 0.8889
components from DWT and odd from DCT). 3 1 0.9889 0.0111 0 0.99 0.9091 1 0.9524
4 0.8 1 0 0.2 0.98 1 0.8 0.8889
5 0.9 0.9889 0.0111 0.1 0.98 0.9 0.9 0.9
6 0.8 0.9667 0.0333 0.2 0.95 0.7273 0.8 0.7619
7 0.9 0.9889 0.0111 0.1 0.98 0.9 0.9 0.9
8 0.6 0.9778 0.0222 0.4 0.94 0.75 0.6 0.6667
9 1 0.9889 0.0111 0 0.99 0.9091 1 0.9524
10 0.7 1 0 0.3 0.97 1 0.7 0.8235
Average 0.84 0.9889 0.0111 0.16 0.974 0.8995 0.84 0.8635
Table 3: Result for Training Time and Matching
Time for ANN
Figure 4: let the left block represents the DCT Number of ANN Training ANN Matching
components and the right block represents the LL Faces Time (Sec.) Time (Sec.)
components of DWT. 10 0.061151 0.011579
According to figure 4 the feature vector will be 20 0.13404 0.017074
formed by [A1, B1, A2, B2, A3, B3ā¦ā¦ā¦..]. 40 0.35419 0.030042
.
4. Like above step these vectors are created for all Table 4: Result for Training Time and Matching
classes of faces. Time for SVM
5. These vectors are used to train the N*(N-1)/2 (N Number of SVM Training SVM Matching
is the number of classes) SVM classifiers as we Faces Time (Sec.) Time (Sec.)
used one against one method. 10 0.53541 0.067786
6. For detection purpose the input image vectors 20 2.2608 0.31951
are calculated in same way as during training and 40 9.2771 1.3173
then it is applied on each classifier.
7. Finally the decision is mode on the basis of VII Conclusion
majority of class returned by N*(N-1)/2 vectors. This paper presents a DCT, DWT Mixed
approach for feature extraction and during the
VI Simulation Results classification phase, the Support Vector Machine
We used the ORL database for testing of (SVM) and Neural Network is tested for robust
our algorithm. The ORL database contains 40 decision in the presence of wide facial
different faces with 10 samples of each face. The variations. The experiments that we have
accuracy of the algorithm is tested for different conducted on the ORL database vindicated that the
number of faces, samples and vector length. SVM method performs better than ANN when
compared for detection accuracy but when
compared for training time and detection time the
neural network out performs the SVM. In future we
can also compare them with using different kernel
The comparison of the proposed method functions and learning techniques.
with Neural Network and PCA based method
shows that the proposed method outperforms the
other two.
Table 1: Result for 40 Faces and 10 Samples
Each using SVM
References
[1] "Biometrics: Overview",
Biometrics.cse.msu.edu. 6 September 2007.
Retrieved 2012-06-10.
www.theijes.com The IJES Page 219
6. Face Recognition using DCT - DWT Interleaved Coefficient Vectors With NN and SVM Classifier
[2] Ignas Kukenys and Brendan McCane āSupport
Vector Machines for Human Face Detectionā,
NZCSRSC ā08 Christchurch New Zealand.
[3] Jixiong Wang (jameswang) āCSCI 1950F Face
Recognition Using Support Vector Machine:
Reportā, Spring 2011, May 24, 2011.
[4] Antony Lam & Christian R. Shelton āFace
Recognition and Alignment using Support
Vector Machinesā, 2008 IE.
[5] Jennifer Huang, Volker Blanz and Bernd
Heisele āFace Recognition Using Component-
Based SVM Classiļ¬cation and Morphable
Modelsā, SVM 2002, LNCS 2388, pp. 334ā
341, 2002. Springer-Verlag Berlin Heidelberg
2002.
[6] Bernd Heisele, Purdy Ho and Tomaso Poggio
āFace Recognition with Support Vector
Machines: Global versus Component-based
Approachā, Massachusetts Institute of
Technology Center for Biological and
Computational Learning Cambridge, MA
02142.
[7] Yongmin Li, Shaogang Gong, Jamie Sherrah
and Heather Liddell āSupport vector machine
based multi-view face detection and
recognitionā, Image and Vision Computing 22
(2004) 413ā427.
[8] Qiong Wang, Jingyu Yang, and Wankou Yang
āFace Detection using Rectangle Features and
SVMā, International Journal of Electrical and
Computer Engineering 1:7 2006.
[9] NataŔa Terzija, Markus Repges, Kerstin Luck,
and Walter Geisselhardt āDIGITAL IMAGE
WATERMARKING USING DISCRETE
WAVELET TRANSFORM:
PERFORMANCE COMPARISON OF
ERROR CORRECTION CODESā,
www.theijes.com The IJES Page 220