Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system
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) t...
| Main Authors: | , , |
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| Format: | Article |
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2003
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| Online Access: | http://shdl.mmu.edu.my/2600/ |
| _version_ | 1848790099624460288 |
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| author | Jin, , ATB Hussain,, A Samad,, SA |
| author_facet | Jin, , ATB Hussain,, A Samad,, SA |
| author_sort | Jin, , ATB |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:07:14Z |
| format | Article |
| id | mmu-2600 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:07:14Z |
| publishDate | 2003 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-26002011-08-24T00:08:26Z http://shdl.mmu.edu.my/2600/ Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system Jin, , ATB Hussain,, A Samad,, SA QA75.5-76.95 Electronic computers. Computer science 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. 2003 Article NonPeerReviewed 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 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Jin, , ATB Hussain,, A Samad,, SA Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system |
| title | Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system |
| title_full | Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system |
| title_fullStr | Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system |
| title_full_unstemmed | Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system |
| title_short | Theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system |
| title_sort | theoretic evidence k-nearest neighbourhood classifiers in a bimodal biometric verification system |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2600/ |