scispace - formally typeset
Search or ask a question
Book ChapterDOI

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.
References
More filters
Book
Vladimir Vapnik1
01 Jan 1995
TL;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 ]....

    [...]

  • ...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....

    [...]

Journal ArticleDOI
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 ]....

    [...]

Journal ArticleDOI
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....

    [...]

  • ...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....

    [...]

  • ...Many researchers claim that when two or more biometric information is combined, recognition accuracy increases [ 1 ] - [23]....

    [...]

  • ...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....

    [...]

  • ...This plot also compares the results with min/max rule based expert fusion [ 1 ]....

    [...]

Journal ArticleDOI
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 ]....

    [...]

Journal ArticleDOI
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]....

    [...]