Biometric Authentication Using Kekre's Wavelet Transform

This paper proposes an enhanced method for personal authentication based on finger Knuckle Print using Kekre's wavelet transform (KWT). Finger-knuckle-print (FKP) is the inherent skin patterns of the outer surface around the phalangeal joint of one's finger. It is highly discriminable and unique which makes it an emerging promising biometric identifier. Kekre's wavelet transform is constructed from Kekre's transform. The proposed system is evaluated on prepared FKP database that involves all categories of FKP. The total database of 500 samples of FKP. This paper focuses the different image enhancement techniques for the pre-processing of the captured images. The proposed algorithm is examined on 350 training and 150 testing samples of database and shows that the quality of database and pre-processing techniques plays important role to recognize the individual. The experimental result calculate the performance parameters like false acceptance rate (FAR), false rejection rate (FRR), True Acceptance rate (TAR), True rejection rate (TRR). The tested result demonstrated the improvement in EER (Error Equal Rate) which is very much important for authentication. The experimental result using Kekre's algorithm along with image enhancement shows that the finger knuckle recognition rate is better than the conventional method.