Abstract
As an individual’s unique biometric, fingerprints are widely used for identification. In recent years, attacks based on forged fingerprints have caused many hidden security risks. Therefore, the detection of forged fingerprints is significant, and fingerprint liveness detection is proposed. Methods based on neural networks have achieved great results, but most of the proposed networks have excessive parameters to meet the needs of practical applications. To this end, this paper designs a lightweight CNN model, whose parameters of the model is only 58 kb, for fingerprint liveness detection. Also, spatial pyramid pooling layer (SPP) is introduced to the networks to enable our network to handle fingerprint images of any size. Meanwhile, this paper propose a HSIC fine-tuning algorithm to initial the parameters of our network before backpropagation, which improves the performance of the network. Experimental results significantly surpass the existing performance, achieving ACE of 2.875 on LivDet 2011 and 1.91 on LivDet 2013.
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Acknowledgment
This work is supported by the National Key R&D Program of China under grant 2018YFB1003205; by the Jiangsu Basic Research Programs-Natural Science Foundation under grant BK20200807; by the Research Startup Foundation of NUIST 2020r015; by the Canada Research Chair Program and the NSERC Discovery Grant; by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund; by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China.
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Yuan, C., Chen, J., Chen, M., Gu, W. (2021). A Lightweight CNN Using HSIC Fine-Tuning for Fingerprint Liveness Detection. 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_27
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DOI: https://doi.org/10.1007/978-3-030-86608-2_27
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