Local Features for Finger-Knuckle-Print Recognition

Researchers have recently found that the finger-knuckle-print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one’s finger, has high discriminability, making it an emerging promising biometric identifier. Effective feature extraction and matching plays a key role in such an FKP based personal authentication system. This chapter studies image local features induced by the phase congruency model, which is supported by strong psychophysical and neurophysiological evidences, for FKP recognition. In the computation of phase congruency, the local orientation and the local phase can also be defined and extracted from a local image patch. These three local features are independent of each other and reflect different aspects of the image local information. We compute efficiently the three local features under the computation framework of phase congruency using a set of quadrature pair filters. We then propose to integrate these three local features by score-level fusion to improve the FKP recognition accuracy. Such kinds of local features can also be naturally combined with Fourier transform coefficients, which are global features. Experiments are performed on the PolyU FKP database to validate the proposed FKP recognition scheme.

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