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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References
Marasco, E., Ross, A.: A survey on antispoofing schemes for fingerprint recognition systems. ACM Comput. Surv. 47(2), 1–36 (2014)
Sousedik, C., Busch, C.: Presentation attack detection methods for fingerprint recognition systems: a survey. IET Biom. 3(4), 219–233 (2014)
Dubey, R., Goh, J., Thing, V.L.L.: Fingerprint liveness detection from single image using low-level features and shape analysis. IEEE Trans. Inf. Forensics Secur. 11(7), 1461–1475 (2016)
Rattani, A., Scheirer, W.J., Ross, A.: Open set fingerprint spoof detection across novel fabrication materials. IEEE Trans. Inf. Forensics Secur. 10(11), 2447–2460 (2015)
Kim, W.: Fingerprint liveness detection using local coherence patterns. IEEE Sig. Process. Lett. 24(1), 51–55 (2017)
Rassem, T.H., Khoo, B.E.: Completed local ternary pattern for rotation invariant texture classification. Sci. World J. 2014(1), 174–175 (2014)
Ojala, T., Pietikinen, M., Menp, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Gragnaniello, D., Poggi, G., Sansone, C., et al.: Local contrast phase descriptor for fingerprint liveness detection. Pattern Recogn. 48(4), 1050–1058 (2015)
Jin, C., Kim, H., Elliott, S.: Liveness detection of fingerprint based on band-selective fourier spectrum. In: Nam, K.-H., Rhee, G. (eds.) ICISC 2007. LNCS, vol. 4817, pp. 168–179. Springer, Heidelberg (2007). doi:10.1007/978-3-540-76788-6_14
Menotti, D., Chiachia, G., Pinto, A.A., et al.: Deep representations for iris, face, and fingerprint spoofing detection. IEEE Trans. Inf. Forensics Secur. 10(4), 864–879 (2015)
Wang, C., Li, K., Wu, Z., Zhao, Q.: A DCNN based fingerprint liveness detection algorithm with voting strategy. In: Yang, J., Yang, J., Sun, Z. (eds.) Biometric Recognition. LNCS, vol. 9428, pp. 241–249. Springer, Cham (2015). doi:10.1007/978-3-319-25417-3_29
Nogueira, R., Lotufo, R., Machado, R.: Fingerprint liveness detection using convolutional neural networks. IEEE Trans. Inf. Forensics Secur. 11(6), 1206–1213 (2016)
Park, E., Kim, W., Li, Q., et al.: Fingerprint liveness detection using CNN features of random sample patches. In: IEEE the 15th International Conference of the Biometrics Special Interest Group, pp. 1–6 (2016)
Marasco, E., Wild, P., Cukic, B.: Robust and interoperable fingerprint spoof detection via convolutional neural networks. In: IEEE International Conference on Technologies for Homeland Security, pp. 1–6 (2016)
Cao, K., Jain, A.K.: Hacking portable phones using 2D printed fingerprints. MSU Technical report, MSU-CSE-16-2 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, pp. 448–456 (2015)
Lin, M., Chen, Q., Yan, S.: Network in network. In: International Conference on Learning Representations. http://arxiv.org/abs/1312.4400 (2014)
Kim, Y.D., Park, E., Yoo, S., et al.: Compression of deep convolutional neural networks for fast and low power portable applications. Comput. Sci. 71(2), 576–584 (2015)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Nair, V., Hinton, E.: Rectified linear units improve restricted Boltzmann machines In: Proceedings of the 27th International Conference on Machine Learning, pp. 807–814 (2010)
Zhang, Y., Zhou, B., Wu, H., Wen, C.: 2D fake fingerprint detection based on improved CNN and local descriptors for smart phone. In: You, Z., Zhou, J., Wang, Y., Sun, Z., Shan, S., Zheng, W., Feng, J., Zhao, Q. (eds.) CCBR 2016. LNCS, vol. 9967, pp. 655–662. Springer, Cham (2016). doi:10.1007/978-3-319-46654-5_72
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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