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.
The main goal of this paper is to authenticate people according to their finger textures. We propose to extract Finger Texture (FT) features of the four finger images (index, middle, ring and little) from a low resolution contactless hand image. Furthermore, we apply a new Image Feature Enhancement (IFE) method to enhance the FTs. The resulting feature image is segmented and a Probabilistic Neural Network (PNN) is employed as an intelligent classifier for recognition. Experimental results illustrate that the proposed approach has superior performance than recent published work. Moreover, the best IFE results were obtained with the Equal Error Rate (EER) equal to 4.07%.
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