Abstract
Abstract Finger knuckle print has recently been seen as an effective biometric technique. In this paper, we propose a hierarchical classification method for finger knuckle print recognition, which is rooted in traditional score-level fusion methods. In the proposed method, we firstly take Gabor feature as the basic feature for finger knuckle print recognition and then a new decision rule is defined based on the predefined threshold. Finally, the minor feature speeded-up robust feature is conducted for these users, who cannot be recognized by the basic feature. Extensive experiments are performed to evaluate the proposed method, and experimental results show that it can achieve a promising performance.
Highlights
A new biometric technology based on finger knuckle print has attracted much attention in the biometrics research community
Compared with traditional biometric techniques, finger knuckle print exhibits some advantages in real application: (1) It is hard to be abraded since people hold stuffs with inner side of their hands
We show the proposed classification method in the case of Gabor and speeded-up robust features (SURF), it can be generalized by two other features with complementarity and even two other biometric techniques
Summary
A new biometric technology based on finger knuckle print has attracted much attention in the biometrics research community. Finger knuckle print is highly unique, so it can be served as a distinctive biometric identifier [1,2,3,4,5]. Compared with traditional biometric techniques (e.g., faces, fingerprints, and voices), finger knuckle print exhibits some advantages in real application: (1) It is hard to be abraded since people hold stuffs with inner side of their hands. Finger knuckle print is considered to be one of the most promising biometric techniques for personal identification in future. Feature extraction and matching play an important role in finger knuckle print recognition. In [2,7], the bandlimited phase-only correlation (BLPOC)-based method was adopted to match finger knuckle print.
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