Fusion of Contour Feature and Edge Texture Information for Palmprint Recognition

A single character cannot describe palmprint features accurately and affect recognition results. To solve this problem, a palmprint recognition method based on fusion of contour feature and edge texture information is proposed. Firstly, mean filter is used to decompose palmprint images to obtain low frequency layer and high frequency layer. Next, the block-based idea is utilized in the two layer images, where gray histogram is used to extract counter features from the low frequency layer, at the same time differential box counting is used to extract texture information from the high frequency layer. Then, these obtained features are fused in order to further improve recognition accuracy. Finally, a common Chi-square measure is used to measure the simlarity. Experimental results on PolyU palmprint database are compared with traditional palmprint recognition algorithm, the proposed method can obtain 99.56% recognition accuracy, the time of feature extraction and matching is only 55.641ms, the effectiveness of the method is proved by lower algorithm complexity and faster speed.