Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system

Jin, , ATB and Hussain,, A and Samad,, SA (2003) Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system. AUDIO-AND VIDEO-BASED BIOMETRIC PERSON AUTHENTICATION, PROCEEDINGS, 2688 . pp. 778-786. ISSN 0302-9743

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Abstract

A bimodal biometric verification system based on facial and vocal biometric modules is described in this paper. The system under consideration is built in parallel where each matching score reported by two classifiers are fused by using theoretic evidence k-NN (tekNN) based on Dempster-Safer (D-S) theory. In this technique, each nearest neighbour of a pattern to be classified is regarded as an item of evidence supporting certain hypotheses concerning the pattern class membership. Unlike statistical based fusion approaches, tekNN based on D-S theory is able to represent uncertainties and lack of knowledge. Therefore, the usage of tekNN leads to a ternary decision scheme, {accept, reject, inconclusive} which provides a more secure protection. From experimental results, the speech and facial biometric modules perform equally well, giving 93.5% and 94.0% verification rates, respectively. A 99.86% recognition rate is obtained when the two modules are fused. In addition, an 'unbalanced' case is been created to investigate the robustness of technique.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 24 Aug 2011 00:08
Last Modified: 24 Aug 2011 00:08
URI: http://shdl.mmu.edu.my/id/eprint/2600

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