Abstract
With the increasing use of fingerprint authentication systems on portable devices, fake fingerprint detection has become growing important because fingerprints can be easily spoofed from a variety of available fabrication materials. Recently, many smartphones are hacked successfully by 2D fake fingerprint, which is a serious threat to authentication security. In order to enhance the robustness against fake fingerprint type, this paper proposes a novel 2D fake fingerprint detection method for portable devices based on improved light Convolutional Neural Networks (CNN). To evaluate the performance of the proposed method, a new 2D fake fingerprint dataset including three fabrication materials is created from capacitive fingerprint scanner. In addition, batch normalization and global average pooling are integrated to optimize the network. Experimental results show that the proposed method has high accuracy, strong robustness and good real-time performance, and can meet the requirements on portable devices.
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Acknowledgement
This work is supported by the Natural Science Foundation of Zhejiang Province, China (No. Y1101304), and the Science and Technology Planning Project of Hebei Province, China (No. 15210124), and the Science and Technology Research Project of Higher School in Hebei Province, China (No. Z2015105), and Hangzhou Commnet Company Limited.
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Zhang, Y., Zhou, B., Qiu, X., Wu, H., Zhan, X. (2017). 2D Fake Fingerprint Detection for Portable Devices Using Improved Light Convolutional Neural Networks. 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_38
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DOI: https://doi.org/10.1007/978-3-319-69923-3_38
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