Palm vein recognition scheme based on an adaptive Gabor filter

Abstract

We propose a novel palm vein recognition scheme based on an adaptive 2D Gabor filter. Three key steps were studied in this scheme: region of interest (ROI) extraction, adaptive Gabor filtering, and template matching. First, in the palm vein image extraction step, the authors used the index finger on both sides of the valley to locate the square area, and then iteratively expanded the area of the square box to maximise the ROI. Second, in the feature extraction step, a novel parameter selection scheme was proposed for optimising the Gabor filter. Third, in the template matching step, the author presented a novel template matching algorithm referred to as the minimum normalised Hamming distance. Experimental results demonstrated that the scheme achieved good performance with an EER of 0.12%.