In this paper, an efficient human authentication method is proposed which utilizes Finger Texture (FT) patterns. This method consists of two essential contributions: a robust and automatic finger extraction method to isolate the fingers from the hand images; and a new feature extraction method based on an Enhanced Local Line Binary Pattern (ELLBP). To overcome poorly imaged regions of the FTs a method is suggested to salvage missing feature elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN) used to perform classification. Three databases have been applied in this paper: PolyU3D2D, IIT Delhi and spectral 460 from Multi-spectral CASIA images. Experimental studies show that the best result was achieved by using ELLBP feature extraction. Furthermore, the salvaging approach proved effective in increasing the verification rate.

No abstract

Finger texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the surrounded patterns code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. Furthermore, a novel classifier termed the re-enforced probabilistic neural network (RPNN) is proposed. It enhances the capability of the standard PNN and provides better recognition performance. Two types of FT images from the multi-spectral Chinese Academy of Sciences Institute of Automation (CASIA) database were employed as two types of spectral sensors were used in the acquiring device: the white (WHT) light and spectral 460 nm of blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the equal error rates at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilising the RPNN.

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.