Finger Vein Recognition Based on Weighted Graph Structural Feature Encoding

The finger-vein recognition performance is usually sensitive to illumination and pose variation. Exploring suitable feature representation method is therefore significant for finger-vein recognition improvement. In this paper, we propose a novel feature encoding method based on local graph structure (LGS), which behaves better in improving the matching accuracy of features. In terms of the variations of veins in running direction, oriented Gabor filters are firstly used for venous region enhancement. Then, a symmetric cross-weighted local graph structure (SCW-LGS) is proposed to locally represent the gradient relationships among the pixels in a neighborhood of the Gabor enhanced images. Based on SCW-LGS, a multi-orientation feature encoding method is developed for vein network feature representation. Experimental results show that the proposed approach achieves better performance than the state-of-the-art approaches on finger-vein recognition.