ROBUST MULTIMODAL BIOMETRIC SYSTEM USING MARKOV CHAIN BASED RANK LEVEL FUSION

Multimodal biometrics is an emerging area of pattern recognition research that aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process. But 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 research, we develop a multimodal biometric system utilizing face, iris and ear features through rank level fusion method. We apply Fisherimage technique on face and par image databases for recognition and Hough transform and Hamming distance techniques for iris image recognition. We introduce Markov chain approach for biometric rank aggregation. We investigate various rank fusion techniques and observe that Markov chain approach gives us the best result. Also this approach satisfies the Condorcet criterion which is essential in any fair rank aggregation system. The system can be effectively used by of security and intelligence services for controlling access to prohibited areas and protecting important national or public information.