Finger Knuckle Print Recognition With Sift and K-Means Algorithm

In general, the identification and verification are done by passwords, pin number, etc., which is easily cracked by others. Biometrics is a powerful and unique tool based on the anatomical and behavioral characteristics of the human beings in order to prove their authentication. This paper proposes a novel recognition methodology of biometrics named as Finger Knuckle print (FKP). Hence this paper has focused on the extraction of features of Finger knuckle print using Scale Invariant Feature Transform (SIFT), and the key points are derived from FKP are clustered using K-Means Algorithm. The centroid of K-Means is stored in the database which is compared with the query FKP K-Means centroid value to prove the recognition and authentication. The comparison is based on the XOR operation. Hence this paper provides a novel recognition method to provide authentication. Results are performed on the PolyU FKP database to check the proposed FKP recognition method.