Efficient person recognition by Finger-Knuckle-Print based on 2D Discrete Cosine Transform

Biometric system has been actively emerging in various industries for the past few years, and it is continuing to roll to provide higher security features for access control system. In the recent years, hand based biometrics is extensively used for personal recognition. In this paper a new biometric system based on texture of the hand knuckles, namely Finger-Knuckle-Print (FKP), is proposed. To extract the image local texture information and represent the FKPs features, the 2D Block based Discrete Cosine Transform (2D-BDCT), is employed. Finally, performance of all finger types is determined individually and a min rule fusion is applied to develop a multimodal system. Experimental results show that 2D-DCT-mod2 yields the best performance for identifying FKPs and it is able to provide an excellent recognition rate and provide more security.