Abstract
With the ever growing deployments of fingerprint recognition systems, presentation attack detection has become the new bottleneck. In order to make full use of the difference in materials between the fake fingerprint and the real fingerprint, we proposed to utilize two images of a finger for classification. A pair of fingerprints are first aligned using a deformable registration algorithm and then are fed into MobileNet-v2 networks to perform the classification. Experimental results on the public dataset LivDet 2011 show that the performance of the proposed approach is promising and prove the effectiveness of fusing two fingerprints rather than using the fingerprints separately.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grants 61622207, 61527808, and Shenzhen fundamental research fund (subject arrangement) (Grant No. JCYJ20170412170438636).
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Zhang, M., Feng, J., Zhou, J. (2019). Fingerprint Presentation Attack Detection via Analyzing Fingerprint Pairs. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_2
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DOI: https://doi.org/10.1007/978-3-030-31456-9_2
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