Deep finger texture learning for verifying people

Abstract

Finger texture (FT) is currently attracting significant attention in the area of human recognition. FT covers the area between the lower knuckle of the finger and the upper phalanx before the fingerprint. It involves rich features which can be efficiently used as a biometric characteristic. In this study, the authors contribute to this growing area by proposing a new verification approach, i.e. deep FT learning. To the best of the authors' knowledge, this is the first time that deep learning is employed for recognising people by using the FT characteristic. Four databases have been used to evaluate the proposed method: the Hong Kong Polytechnic University Contact-free 3D/2D (PolyU2D), Indian Institute of Technology Delhi (IITD), CASIA Blue spectral (CASIA-BLU) corresponding to spectral 460 nm and CASIA White spectral (CASIA-WHT) from the CASIA Multi-Spectral images database. The obtained results have shown superior performance compared with recent literature. The verification accuracies have attained 100, 98.65, 100 and 98% for the four databases of PolyU2D, IITD, CASIA-BLU and CASIA-WHT, respectively.