Markov chain model for multimodal biometric rank fusion

Multimodal biometric aims at increasing reliability of biometric systems through utilizing more than one biometric in decision-making process. An effective fusion scheme is necessary for combining information from various sources. Such information can be integrated at several distinct levels, such as sensor level, feature level, match score level, rank level, and decision level. In this paper, we present a multimodal biometric system utilizing face, iris, and ear biometric features through rank level fusion method using novel Markov chain approach. We first apply fisherimage technique to face and ear image databases for recognition and Hough transform and Hamming distance techniques for iris image recognition. The main contribution is in introducing Markov chain approach for biometric rank aggregation. One of the distinctive features of this method is that it satisfies the Condorcet criterion, which is essential in any fair rank aggregation system. The experimentation shows superiority of the proposed approach to other recently introduced biometric rank aggregation methods. The developed system can be effectively used by security and intelligence services for controlling access to prohibited areas and protecting important national or public information.