Abstract
The randomness of vein networks determines the discrimination of finger veins patterns in recognition. Effectively describing the random patterns is therefore very important for finger-vein based biometrics. In this paper, a new graph-based method is proposed for finger-vein network feature representation. A block-wise action is first done for graph node generation from a finger-vein image. By applying Delaunay triangulation to these obtained nodes, the graph edges are then built for featuring the spatial relations between images blocks. For a given feature space, each of these edges can locally represent a relationship between two adjacent nodes. Considering local variations in image contents, the graph edges are further weighted node-wisely using the statistics of image blocks. Thus, a graph can globally represent a finger-vein network, and its weighted edges can locally describe the relations of image blocks. Experimental results on two image databases totally 1,200 image samples show that the proposed method performs well in finger-vein recognition.
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Acknowledgements
This work is jointly supported by National Natural Science Foundation of China (Nos. 61379102, U1433120, 61502498) and the Fundamental Research Funds for the Central Universities (No. 3122014C003).
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Ye, Z., Yang, J., Palancar, J.H. (2017). Weighted Graph Based Description for Finger-Vein Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_40
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DOI: https://doi.org/10.1007/978-3-319-69923-3_40
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