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Conventional multimodal biometric identification systems tend to have larger memory footprint, slower processing speeds and a higher implementation and operational cost. In this paper we propose a state of the art framework for multimodal biometric identification system which can be adapted for any type of biometrics to provide smaller memory footprint and faster implementation than the conventional multimodal biometrics systems. The proposed framework is verified by development of a fingerprint and iris fusion system which utilizes a single hamming distance based matcher to provide higher accuracy than the individual unimodal system.
Date of Conference: 20-21 Aug. 2009