A bi-modal palmvein palmprint biometric human identification based on fusing new CDSDP features

Uni-modal analysis of palmprint and palmvein has been investigated for human recognition. One of the problems encountered with such system is that the Uni-modal biometric is less perfect, reliable and vulnerable to spoofing, as the data can be imitated or forged. In this paper, we present a multi-modal Personal identification system using palmprint and palmvein images with their fusion applied at feature level. The feature vectors of palmprint and palmvein images are extracted with Circular Difference and Statistical Directional Patterns descriptor. Experimental results show that the proposed system provides a better representation and achieves lower error rates in palm recognition. Furthermore, the proposed multimodal method outperforms any of its individual modality.