Design of multimodal biometrics system based on feature level fusion

Multimodal system aims to fuse two or more biometrics traits of an individual to achieve improvement in FAR and FRR of biometrics system which in turn increases accuracy of system. In this paper we have proposed biometrics system based on biometrics traits face and signature. The performances of face and signature recognition can be enhanced using a proposed feature selection method to select an optimal subset of features. Signature is very important human characteristics which is required in all financial transaction for human identification. In case of financial transaction correct recognition is necessary otherwise it can lead to fraudulent activities. Face is most commonly acceptable and popular biometrics. Proposed algorithm fuses wavelet based features of face and signature. Wavelet based feature fusion method also gave very promising results. Hamming distance classifier is used to take decision whether person is genuine or imposter. Our experiments show that the proposed algorithm can achieve higher classification accuracy than offline signature and face based identification system. We have achieved false accept rate of 5.99% and 3% for multibiometrics system for ORL databases combined with Caltech and Ucoer real signature database resp.