In this study, an efficient human authentication method is proposed which utilises 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 used to perform classification. Three databases have been applied in this study: 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.
['Raid Al-Nima', 'Satnam Singh Dlay', 'Saadoon A. M. Al-Sumaidaee', 'Wai Lok Woo', 'Jonathon A. Chambers']
Robust feature extraction and salvage schemes for finger texture based biometrics