Abstract
Face anti-spoofing detection gains more and more attention, and due to some attacks like spoofing image, spoofing video and 3D masks etc. in security sensitive area, it has being widely used in industries in China. In this work, we summarize the traditional and recent methods proposed in the area of face anti-spoofing and divide them into three main categories, which are feature based methods, deep learning based methods and other methods. In addition, we also compare the performance of these methods and investigate the application of some famous Chinese enterprises. Finally, we provide an outlook into the future of this field of research.
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References
Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746–761 (2015)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans. Inf. Forensics Secur. 11(8), 1 (2016)
Bharadwaj, S., Dhamecha, T.I., et al.: Face Anti-spoofing via motion magnification and multifeature videolet aggregation (2014)
Tirunagari, S., Poh, N., Windridge, D., et al.: Detection of face spoofing using visual dynamics. IEEE Trans. Inf. Forensics Secur. 10(4), 762–777 (2015)
Li, X., Komulainen, J., Zhao, G., et al.: Generalized face anti-spoofing by detecting pulse from face videos. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE (2016)
Xu, Z., Li, S., Deng, W.: Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE (2015)
Souza, G.B.D., Papa, J.P., Marana, A.N.: On the learning of deep local features for robust face spoofing detection (2018)
Atoum, Y., Liu, Y., Jourabloo, A., et al.: Face anti-spoofing using patch and depth-based CNNs. In: IEEE International Joint Conference on Biometrics. IEEE (2018)
Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision (2018)
Xiao, S., Xu, Z., Liangji, F., et al.: Discriminative representation combinations for accurate face spoofing detection. Pattern Recognition S0031320318303182 (2018)
Jourabloo, A., Liu, Y., Liu, X.: Face de-spoofing: anti-spoofing via noise modeling (2018)
Sun, X., Huang, L., Liu, C.: Context based face spoofing detection using active near-infrared images. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE (2016)
Hernandez-Ortega, J., Fierrez, J., Morales, A., et al.: Time analysis of pulse-based face anti-spoofing in visible and NIR. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE Computer Society (2018)
Kim, S., Ban, Y., Lee, S.: Face liveness detection using a light field camera. Sensors 14(12), 22471–22499 (2014)
Xie, X., Gao, Y., Zheng, W.S., et al.: One-snapshot face anti-spoofing using a light field camera (2017)
Nagpal, C., Dubey, S.R.: A performance evaluation of convolutional neural networks for face anti spoofing (2018)
Arora, A.S., Singh, M.: A novel face liveness detection algorithm with multiple liveness indicators. Wirel. Personal Commun. 100(4), 1677–1687 (2018)
Boulkenafet, Z.: A competition on generalized software-based face presentation attack detection in mobile scenarios. In: ICJB. IEEE (2017)
Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: CVPR. IEEE (2018)
de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: Can face anti-spoofing countermeasures work in a real world scenario? In: ICB. IEEE (2013)
Bharadwaj, S., Dhamecha, T.I., Vatsa, M., Singh, R.: Computationally efficient face spoofing detection with motion magnification. In: CVPRW. IEEE (2013)
Pinto, A., Pedrini, H., Schwartz, W.R., Rocha, A.: Face spoofing detection through visual codebooks of spectral temporal cubes. IEEE Trans. Image Process. 24(12), 4726–4740 (2015)
Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. Comput. Sci. 9218, 373–384 (2014)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: ICIP. IEEE (2015)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans. Inf. Forensics Secur. 11(8), 1818–1830 (2016)
Acknowledgement
This work is supported by two funding: the 13th Five-Year Plan of Education Science in Guangdong Province in 2017 (Contextual Moral Education Game Oriented to Collaborative Construction and Its Application, No. 2017JKDY43) and the Guangdong Postgraduate Education Innovation Project in 2018 (New Media and New Technologies in eLearning).
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Wu, B., Pan, M., Zhang, Y. (2020). A Review of Face Anti-spoofing and Its Applications in China. In: Kim, J., Geem, Z., Jung, D., Yoo, D., Yadav, A. (eds) Advances in Harmony Search, Soft Computing and Applications. ICHSA 2019. Advances in Intelligent Systems and Computing, vol 1063. Springer, Cham. https://doi.org/10.1007/978-3-030-31967-0_4
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DOI: https://doi.org/10.1007/978-3-030-31967-0_4
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