Finger Vein Presentation Attack Detection Based on Texture Analysis

Biometrics is an effective way to identify and authenticate users based on their personal traits. Among all kinds of hand-based biometrics, finger vein appears to be emerging biometrics that has received a great attention due to its rich information available and ease for implementation. With finger vein system becoming more and more popular, there have been various attempts to comprise the system. Recent studies reveal the vulnerabilities of finger vein system to presentation attack where the sensory device accepts a fake printed finger vein image and gives access as if it were a genuine attempt. In this study, a presentation attack detection method based on hybrid feature spaces of finger vein texture analysis is proposed. Histogram of oriented gradient operator is applied on different channels of grayscale and color feature spaces to obtain texture information of the histogram descriptors. The proposed method includes two implementations of feature space analysis, namely CHOG1 and CHOG2. A well-established publicly available dataset is used to analysis and evaluate the proposed implementations. Experimental results suggest that the combination channels of grayscale and color luminance is able to generate better performance through Support Vector Machine classifier with ACER as low as 0.60% and 0.74% for CHOG1 and CHOG2, respectively. The experiments show that the implementation of CHOG1 performs slightly better than single channel max gradients of CHOG2.