On-line Finger-Knuckle-Print Identification Using Gaussian Mixture Models & Discrete Cosine Transform

Abstract

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, we propose an efficient online personal identification system based on Finger-Knuckle- Print (FKP) using the Gaussian Mixture Model (GMM ) and two-dimensional Block Based Discrete Cosine Transform (2D- BDCT ). In this study, a segmented FKP is firstly divided into non-overlapping and equal-sized blocks, and then, applies the 2D-BDCT over each block. By using zigzag scan order each transform block is reordered to produce the feature vector. Subsequently, we use the GMM for modeling the feature vector of each FKP. Finally, Log-likelihood scores are used for FKP matching. Experimental results show that our proposed method yields the best performance for identifying FKPs and it is able to provide an excellent identification rate and provide more security.