Abstract
Graph-based method is highly favorable for finger-vein recognition. The existing graph construct strategies are inflexible and unable to describe the vein networks effectively. In this paper, we propose a new weighted graph construction method for finger-vein network feature representation. First, a node-set generated by image division is reshaped according local vein-network skeleton. Then, the edges connecting adjacent nodes are weighted by considering both the content variations of blocks and the similarities between adjacent blocks. Therefore, the generated graphs using these nodes and weighted edges are capable in carrying both the global random patterns and the local variations contained in finger-vein networks. Experiments are implemented to show that the proposed method achieves better results than other existing methods.
Supported by the National Natural Science Foundation of China under Grant 62076166.
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Ye, Z., Zhao, Z., Wen, M., Yang, J. (2022). Weighted Graph Based Feature Representation for Finger-Vein Recognition. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_38
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