Fuzzy Fusion in Multimodal Biometric Systems

Multimodal authentication systems represent an emerging trend for information security. These systems could replace conventional mono-modal biometric methods using two or more features for robust biometric authentication tasks. They employ unique combinations of measurable physical characteristics: fingerprint, facial features, iris of the eye, voice print, hand geometry, vein patterns, and so on. Since these traits are hardly imitable by other persons, the aim of these multibiometric systems is to achieve a high reliability to determine or verify person's identity. In this paper a multimodal biometric system using two different fingerprints is proposed. The matching module integrates fuzzy logic methods for matching score fusion. Experimental trials using both decision level fusion and matching score level fusion were performed. Experimental results show an improvement of 6.7% using the matching score level fusion rather then a mono-modal authentication system