Intelligent Biometric Information Fusion using Support Vector Machine
01 Jan 2007-pp 325-349
About: The article was published on 2007-01-01. It has received 27 citations till now. The article focuses on the topics: Support vector machine & Biometrics.
Citations
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12 May 2013
TL;DR: The obtained results show the effectiveness of the OC-SVM compared to the standard two-class SVM classifier as well as to other score fusion schemes.
Abstract: Different single biometric systems carry in their outputs redundant and complementary information. The concatenation of match scores from various systems in a single feature vector to feed the classifiers can provide an opportunity to develop more efficient system compared to other fusion schemes. In this work, we investigate the performance of classifier based biometric score fusion. For this purpose, the One-Class SVM (OC-SVM) classifier is employed since, in the general case of biometric systems, the data are highly unbalanced or available from only a single class. Experiments are conducted on the well known NIST-multimodal partition of the BSSRI database and results are reported using genuine acceptance and false acceptance criteria. The obtained results show the effectiveness of the OC-SVM compared to the standard two-class SVM classifier as well as to other score fusion schemes.
2 citations
Cites background from "Intelligent Biometric Information F..."
...Keywords-Multimodal biometrics; one class support vector machine; match score; normalization; fusion....
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01 Jan 2008
TL;DR: To address the challenge of facial aging, an age transformation algorithm is proposed that registers two face images and minimizes the aging variations and the concept of online learning is introduced to address the problem of classifier re-training and update.
Abstract: Mitigating the Effect of Covariates in Face Recognition By Richa Singh Current face recognition systems capture faces of cooperative individuals in controlled environment as part of the face recognition process. It is therefore possible to control lighting, pose, background, and quality of images. However, in a real world application, we have to deal with both ideal and imperfect data. Performance of current face recognition systems is affected for such non-ideal and challenging cases. This research focuses on designing algorithms to mitigate the effect of covariates in face recognition. To address the challenge of facial aging, an age transformation algorithm is proposed that registers two face images and minimizes the aging variations. Unlike the conventional method, the gallery face image is transformed with respect to the probe face image and facial features are extracted from the registered gallery and probe face images. The variations due to disguises cause change in visual perception, alter actual data, make pertinent facial information disappear, mask features to varying degrees, or introduce extraneous artifacts in the face image. To recognize face images with variations due to age progression and disguises, a granular face verification approach is designed which uses dynamic feed-forward neural architecture to extract 2D log polar Gabor phase features at different granularity levels. The granular levels provide non-disjoint spatial information which is combined using the proposed likelihood ratio based Support Vector Machine match score fusion algorithm. The face verification algorithm is validated using five face databases including the Notre Dame face database, FG-Net face database and three disguise face databases. The information in visible spectrum images is compromised due to improper illumination whereas infrared images provide invariance to illumination and expression. A multispectral face image fusion algorithm is proposed to address the variations in illumination. The Support Vector Machine based image fusion algorithm learns the properties of the multispectral face images at different resolution and granularity levels to determine optimal information and combines them to generate a fused image. Experiments on the Equinox and Notre Dame multispectral face databases show that the proposed algorithm outperforms existing algorithms. We next propose a face mosaicing algorithm to address the challenge due to pose variations. The mosaicing algorithm generates a composite face image during enrollment using the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a users face image. Experiments conducted on three different databases indicate that face mosaicing offers significant benefits by accounting for the pose variations that are commonly observed in face images. Finally, the concept of online learning is introduced to address the problem of classifier re-training and update. A learning scheme for Support Vector Machine is designed to train the classifier in online mode. This enables the classifier to update the decision hyperplane in order to account for the newly enrolled subjects. On a heterogeneous near infrared face database, the case study using Principal Component Analysis and C2 feature algorithms shows that the proposed online classifier significantly improves the verification performance both in terms of accuracy and computational time.
Cites background from "Intelligent Biometric Information F..."
...Further, several fusion algorithms have been proposed to fuse the information extracted from visible and LWIR face images at image level [41], [43], [94], feature level [94], [95], match score level [95], and decision level [95]....
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References
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01 Jan 1995TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
40,147 citations
"Intelligent Biometric Information F..." refers background or methods in this paper
...and C is the factor used to control the violation of safety margin rule [ 33 ]....
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...Support Vector Machine, proposed by [ 33 ], is a powerful methodology for solving problems in nonlinear classification, function estimation and density 330 R. Singh et al....
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TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Abstract: We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.
11,674 citations
"Intelligent Biometric Information F..." refers methods in this paper
...These plots show that the performance of both the phase and amplitude features are comparable and they outperform the standard PCA and LDA based face recognition algorithms [ 46 ]....
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TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
Abstract: We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.
5,670 citations
"Intelligent Biometric Information F..." refers background or methods or result in this paper
...In [ 1 ], Kittler proposed a set of matching score fusion rules to combine the classifier which includes majority voting, sum rule, and product rule....
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...Here the parameter C is replaced by another parameter ν� [0, 1 ] which is the lower bound on the fraction of support vectors and upper bound on the number of fraction of margin errors....
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...Many researchers claim that when two or more biometric information is combined, recognition accuracy increases [ 1 ] - [23]....
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...It has been suggested that the fusion of match scores of two or more classifiers gives better performance over a single classifier [ 1 , 2]. In general, match score fusion is performed using sum rule, product rule or other statistical rules....
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...This plot also compares the results with min/max rule based expert fusion [ 1 ]....
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TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.
4,816 citations
"Intelligent Biometric Information F..." refers methods in this paper
...To study the performance of various levels of fusion, experiments are performed using two face databases: • Frontal face images from the colored FERET database [ 43 ]....
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TL;DR: A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.
Abstract: We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.
2,737 citations
"Intelligent Biometric Information F..." refers methods in this paper
...One alternative and intuitive approach to solve this problem is the use of ν-SVM of a soft margin variant of the optimal hyperplane which uses the ν-parameterization [ 35 ] and [36]....
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