Intelligent Biometric Information Fusion using Support Vector Machine
References
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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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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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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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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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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