Abstract
Among existing finger vein recognition methods, the keypoint-based method has gained much attention owning to its deformation-tolerance. However, the adopted keypoint descriptors may be insufficient in keypoint quantity and descriptor distinctiveness, since they are generally artificially-designed and sensitive to the poor quality of the finger vein images. To address the above-mentioned problem, an automatically-learned keypoint-based method utilizing fully convolutional neural networks (FCNNs), called SuperPoint-based finger vein recognition (SP-FVR), is proposed in this paper. We first adjust the finger vein images by pertinent intensity correction, noise removal and resizing. Then, we locate and learn the keypoint descriptors utilizing the SuperPoint model. Finally, the keypoints are matched by a bilateral strategy. Experiments on the HKPU and SDU-MLA databases demonstrate the effectiveness of the proposed method in real applications, the EERs are 0.0025 and 0.0138, with the FRRs-at-0-FAR of 0.0218 and 0.0890, respectively.
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Acknowledgments
This work is supported by the Natural Science Foundation of China under Grant Nos. 61801263 and 61976123, Taishan Young Scholars Program of Shandong Province and the Key Development Program for Basic Research of Shandong Province (ZR2020ZD44).
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Meng, X., Yuan, S. (2022). SP-FVR: SuperPoint-Based Finger Vein Recognition. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_11
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DOI: https://doi.org/10.1007/978-3-031-20233-9_11
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