Tri-modal biometric fusion for human authentication by tracking Differential Code Pattern

Human authentication can now be seen as a crucial social problem. In this paper a multimodal authentication system is presented which is highly reliable and fuses iris, finger-knuckle-print and palmprint image matching scores. Segmented ROI are preprocessed using DCP (Differential Code Pattern) to obtain robust corner features. Later they are matched using the GOF (Global Optical Flow) based dissimilarity measure. The proposed system has been tested on Casia Interval and Lamp iris, PolyU finger-knuckle-print and PolyU and Casia palmprint, public databases. The proposed system has shown good performance over all unimodal databases while over multimodal (fusion of all three) databases it has shown perfect performance (i.e. CRR = 100% with EER = 0%).