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Proceedings Paper

A multi-algorithm-based automatic person identification system
Author(s): Md. Maruf Monwar; Marina Gavrilova
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Paper Abstract

Multimodal biometric is an emerging area of research that aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process. In this work, we develop a multi-algorithm based multimodal biometric system utilizing face and ear features and rank and decision fusion approach. We use multilayer perceptron network and fisherimage approaches for individual face and ear recognition. After face and ear recognition, we integrate the results of the two face matchers using rank level fusion approach. We experiment with highest rank method, Borda count method, logistic regression method and Markov chain method of rank level fusion approach. Due to the better recognition performance we employ Markov chain approach to combine face decisions. Similarly, we get combined ear decision. These two decisions are combined for final identification decision. We try with 'AND'/'OR' rule, majority voting rule and weighted majority voting rule of decision fusion approach. From the experiment results, we observed that weighted majority voting rule works better than any other decision fusion approaches and hence, we incorporate this fusion approach for the final identification decision. The final results indicate that using multi algorithm based can certainly improve the recognition performance of multibiometric systems.

Paper Details

Date Published: 19 April 2010
PDF: 6 pages
Proc. SPIE 7701, Visual Information Processing XIX, 770109 (19 April 2010); doi: 10.1117/12.850669
Show Author Affiliations
Md. Maruf Monwar, Univ. of Calgary (Canada)
Marina Gavrilova, Univ. of Calgary (Canada)


Published in SPIE Proceedings Vol. 7701:
Visual Information Processing XIX
Zia-ur Rahman; Stephen E. Reichenbach; Mark A. Neifeld, Editor(s)

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