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Inner-knuckle-print for human authentication by using ring and middle fingers

This paper proposed a new approach for inner-knuckle-print recognition. Theinner knuckle print is one of the reliable physiological characteristics among different approaches that exist in biometric. In this paper, the image of the inner surface of the middle and ring fingers are used for human verification. We considered the inner knuckle print as a texture and applied two types of feature extraction methods, namely Gabor wavelet filters and wavelet energy. Among all feature that is extracted by these approaches, fifty superior features selected by the forward feature selection algorithm. Features are classified with a new approach by using K-nearest neighbor, fuzzy K-nearest neighbor, parzen window and support vector machine classifiers. In Hong Kong Polytechnic University Contact-free 3D/2D Hand Images testing database of 1770 whole hand samples from 177 subjects, we achieved Equal Error Rate of 4.79%±1.74, 6.14%±0.09, 3.70±0.57 by using K-Nearest Neighbor, parzen window and support vector machine classifier for ring, middle and both of them together respectively.