Multiple texture information fusion for finger-knuckle-print authentication system

This paper proposes a finger-knuckle-print based authentication system by fusing multiple texture features. It contains new algorithms for extracting region of interest (ROI) with the help of curvature Gabor filters, image quality parameters, ROI enhancement using gradient based ordinal relationships, and dissimilarity measure for matching. The proposed system has been tested on the largest publicly available finger-knuckle-print PolyU database consisting of 7920 finger-knuckle-print images obtained from 165 subjects in two sessions. It has shown good performance.