Abstract
With the rapid development of face recognition systems in various practical applications, numerous face spoofing attacks under different environment and devices have emerged. The countermeasure of face spoofing attacks in cross-database have caused increasing attention. This paper proposes a face spoofing detection method with motion analysis based cross-database voting. We employ the consistency motion information of different databases like eye-blink, mouth movements and facial expression etc. Then the motion information maps of a video is classified to real or fake by CNN model. Furthermore, cross-database voting strategy is constructed to transfer motion characteristics from a database to another for face spoofing inference. Experimental results demonstrate that the proposed method outperforms its comparisons taking benefits of motion analysis based CNN classification and cross-database voting.
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This work was supported in part by the Beijing Municipal Education Commission Science and Technology Innovation Project under Grant KZ201610005012.
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Wu, L., Xu, Y., Jian, M., Cai, W., Yan, C., Ma, Y. (2017). Motion Analysis Based Cross-Database Voting for Face Spoofing Detection. 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_57
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DOI: https://doi.org/10.1007/978-3-319-69923-3_57
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