Local vein Texton learning for finger vein recognition

Abstract

In finger vein recognition, the input image is generally labeled in accordance with the nearest enrolled neighbor. However, it is so rigid that it is inadequate for some cases. This paper explores a modified sparse representation method for finger vein recognition. In the method, each block in a finger vein image will be sparsely represented by dictionary textons, not simply labeled by the nearest enrolled block, and the representation coefficients of all blocks are arranged to be a two-dimensional histogram to model the image. As textons is learned from local vein pattern, not global vein pattern. Therefore, for encode global geometric information of finger vein pattern, the representation coefficient histogram is projected to different lines, and then connected in parallel to generate more powerful image features. Extensive experiments on the HKPU finger vein database show the effectiveness of the modified sparse representation method in finger vein recognition.