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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.
Citations
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Journal ArticleDOI
TL;DR: A fast fingerprint verification algorithm using level-2 minutiae and level-3 pore and ridge features using a feature supervector yields discriminatory information and higher accuracy compared to existing recognition and fusion algorithms.

34 citations


Cites methods or result from "Intelligent Biometric Information F..."

  • ...To construct an optimal hyperplane, SVM uses an iterative training algorithm that maximizes the margin between two classes [22]....

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  • ...We also compare the proposed fusion algorithm with sum rule [14] and SVM based match score fusion algorithm [22]....

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  • ...Support vector machine, proposed by Vapnik [21], is a powerful methodology for solving problems in non-linear classification, function estimation, and density estimation [22]....

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  • ...In our previous research, we have shown that a variant of support vector machine known as the dual n-SVM (2n-SVM) is useful for classification in biometrics [22,23]....

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  • ...Further, sum rule [14] and SVM match score fusion [22] compares the performance with match score level fusion algorithms....

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Journal ArticleDOI
12 Dec 2007
TL;DR: In this article, an evidence-theoretic multimodal unification approach using belief functions that take into account the variability in biometric image characteristics is proposed to dynamically select the most appropriate fusion algorithm for a given scenario.
Abstract: This paper formulates an evidence-theoretic multimodal unification approach using belief functions that take into account the variability in biometric image characteristics. While processing nonideal images, the variation in the quality of features at different levels of abstraction may cause individual classifiers to generate conflicting genuine-impostor decisions. Existing fusion approaches are nonadaptive and do not always guarantee optimum performance improvements. We propose a contextual unification framework to dynamically select the most appropriate evidence-theoretic fusion algorithm for a given scenario. In the first approach, the unification framework uses deterministic rules to select the most appropriate fusion algorithm; while in the second approach, the framework intelligently learns from the input evidences using a 2nu-granular support vector machine. The effectiveness of the unification approach is experimentally validated by fusing match scores from level-2 and level-3 fingerprint features. Compared to existing fusion algorithms, the proposed unification approach is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered.

31 citations

01 Jan 2012
TL;DR: The issues in the designing of a video authentication system include the classification of tampering attacks, levels of tampering attack and robustness, and the categorization of existing video authentication techniques with their shortcomings are presented.
Abstract: Video authentication aims to ensure the trustworthiness of the video by verifying the integrity and source of video data. It has gained much attention in the recent years. In this paper we present the issues in the designing of a video authentication system. These issues include the classification of tampering attacks, levels of tampering attack and robustness. Further we present the categorization of existing video authentication techniques with their shortcomings. Moreover we have also given the challenging scenarios in which the video authentication would be a critical task.

28 citations

Journal ArticleDOI
TL;DR: A brief survey on video authentication techniques with their classification is presented, broadly classified into four categories: digital signature based techniques, watermarkbased techniques, intelligent techniques and other techniques.
Abstract: With the innovations and development in sophisticated video editing technology and a wide spread of video information and services in our society, it is becoming increasingly significant to assure the trustworthiness of video information. Therefore in surveillance, medical and various other fields, video contents must be protected against attempt to manipulate them. Such malicious alterations could affect the decisions based on these videos. A lot of techniques are proposed by various researchers in the literature that assure the authenticity of video information in their own way. In this paper we present a brief survey on video authentication techniques with their classification. These authentication techniques are broadly classified into four categories: digital signature based techniques, watermark based techniques, intelligent techniques and other techniques. Furthermore we give the shortcomings of different categories of video authentication techniques in brief.

20 citations

Journal ArticleDOI
TL;DR: An intelligent video authentication algorithm which integrates learning based Support Vector Machine classification with Singular Value Decomposition watermarking is presented which outperforms existing video authentication algorithms.
Abstract: In law enforcement applications such as surveillance and forensics, video is often presented as evidence. It is therefore of paramount importance to establish the authenticity and reliability of the video data. This paper presents an intelligent video authentication algorithm which integrates learning based Support Vector Machine classification with Singular Value Decomposition watermarking. During video capture and storage, intrinsic local correlation information is extracted from the frames and embedded in the frames at local levels. Tamper detection and classification is performed using the inherent video information and embedded correlation information. The proposed algorithm is independent of the choice of watermark and does not require any key to store. Further, it is robust to global tampering such as frame addition and removal, local attacks such as object alteration and can differentiate between acceptable operations and malicious tampering. Experiments are performed on an extensive database which contains non-tampered videos and videos with several types of tampering. The results show that the proposed algorithm outperforms existing video authentication algorithms.

18 citations


Cites background or methods from "Intelligent Biometric Information F..."

  • ...Further, to construct an optimal hyperplane, we use an iterative training algorithm that maximizes the margin between two classes [17]....

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  • ...Additional details of SVM can be found in [17, 22]....

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  • ...SVM, proposed by Vapnik [22], is a powerful technique for non-tampered classification, function estimation and density estimation [17]....

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References
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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 ]....

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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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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 ]....

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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....

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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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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 ]....

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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]....

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