Palm vein recognition method based on fusion of local Gabor histograms

Abstract

Gabor features have been shown to be effective for palm vein recognition. This paper presents a novel feature representation method, implementing the fusion of local Gabor histograms (FLGH), in order to improve the accuracy of palm vein recognition systems. A new local descriptor called local Gabor principal differences patterns (LGPDP) encodes the Gabor magnitude using the local maximum difference (LMD) operator. The corresponding Gabor phase patterns are encoded by local Gabor exclusive OR (XOR) patterns (LGXP). Fisher's linear discriminant (FLD) method is then implemented to reduce the dimensionality of the feature representation. Low-dimensional Gabor magnitude and phase feature vectors are finally fused to enhance accuracy. Experimental results from Institute of Automation, Chinese Academy of sciences (CASIA) database show that the proposed FLGH method achieves better performance by utilizing score-level fusion. The equal error rate (EER) is 0.08%, which outperforms other conventional palm vein recognition methods (EER range from 2.87% to 0.16%), e.g., the Laplacian palm, minutiae feature, Hessian phase, Eigenvein, local invariant features, mutual foreground local binary patterns (LBP), and multi-sampling feature fusion methods.