A Finger Vein Recognition Method Based on Histogram of Oriented Lines and (2D)~2FPCA

A finger vein recognition method based on two-dimensional histogram of oriented lines (2DHOL) and two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA) is proposed in this paper. Firstly, according to the characteristics of different finger vein lines, the calculation method of gradient amplitude and direction in the histogram gradient histogram (HOG) is improved. The line responses and orientation of pixels in finger vein images are extracted by adopting the real part of 2-D Gabor filter. In this way, 2DHOL features are obtained. Secondly, considering the correlation between columns and rows of a finger vein image, and combining with the image’s category information, the dimension of 2DHOL features is reduced by employing (2D)2FPCA. Finally, Euclidean distance is utilized in recognition. Experimental results applying different finger vein image databases show that, the proposed method can effectively improve the accuracy of finger vein recognition, and enhance the robustness to quantity changes of training samples.