Face and Speech Recognition Fusion Method Based on Penalty Coefficient

doi:10.3969/j.issn.1006-2475.2015.11.015

Abstract

Abstract: The quality of biometric sample acquired from different acquisition devices is higher, then the reliability of recognition is higher. For the same biometric sample, recognition method is better, then the reliability of recognition is higher. So this paper proposes a multi-biometric recognition algorithm using biometric sample quality and recognition expert reliability (QSVM). First, the method obtains the sample penalty coefficient and reliability penalty coefficients from the sample quality and the expert reliability, then deduces the overall penalty coefficient, finally, uses the overall penalty coefficient to modify SVM fusion recognition algorithm. The experiment uses the XM2VTS database. We compares the Half Total Error Rate (HTER) of QSVM, Bayesian, Fisher linear discriminant, multi-layer perceptron, mean methods and SVM, the experimental results show that the HTER of QSVM fusion algorithm is lower.
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Key words: multi-modal biometric recognition, face recognition, speech recognition, penalty coefficient, SVM

CLC Number:

TP391.4

WEN Miao-i1,2. Face and Speech Recognition Fusion Method Based on Penalty Coefficient[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.11.015.

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