Feature Level Fusion in Biometric Systems

Multimodal biometric systems utilize the evidence presented by multiple biometric sources (e.g., face and fingerprint, multiple fingers of a user, multiple impressions of a single finger, etc.) in order to determine or verify the identity of an individual. Information from multiple sources can be consolidated in three distinct levels [1]: (i) feature extraction level; (ii) match score level; and (iii) decision level. While fusion at the match score and decision levels have been extensively studied in the literature, fusion at the feature level is a relatively understudied problem. In this paper we present a novel technique to perform fusion at the feature level by considering two biometric modalities face and hand geometry. Preliminary results indicate that the proposed technique can lead to substantial improvement in multimodal matching performance.