Abstract
Finger vein recognition has advantages that can’t be replaced by other biometrics. Our designed system employs the feature vectorization pattern and multiple strategies, such as Arcloss, image enhancement designed in this paper and luminance-inversion data augmentation. Compared with the other algorithms using classifiers, this system is open-set testable and doesn’t require additional computer resource for new category registration, which is more in line with practical application requirements. The recognition rate of this system is up to 99.8% in closed-set test on two public databases, and the recognition rate keeps acceptable when the training samples are reduced. The recognition rate can reach about 95% in cross open-set test. This paper also proposes two optimal threshold determination strategies to determine whether a category is registered or not.
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Chen, Z., Yu, W., Bai, H., Li, Y. (2021). An Arcloss-Based and Openset-Test-Oriented Finger Vein Recognition System. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_32
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