A Review on Face Anti-Spoofing

https://doi.org/10.22146/ijitee.61827

Rizky Naufal Perdana(1*), Igi Ardiyanto(2), Hanung Adi Nugroho(3)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author

Abstract


The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.

Keywords


Image Processing;Biometric System;Face Spoof

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References

J. Stehouwer, A. Jourabloo, Y. Liu, and X. Liu, “Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2020, pp. 7292–7301.

M.O. Oloyede, G.P. Hancke, and H.C. Myburgh, “A Review on Face Recognition Systems: Recent Approaches and Challenges,” Multimed. Tools Appl., Vol. 79, No. 37–38, pp. 27891–27922, 2020.

L. Souza, L. Oliveira, M. Pamplona, and J. Papa, “How Far did We Get in Face Spoofing Detection?” Eng. Appl. Artif. Intell., Vol. 72, pp. 368–381, 2018.

Y. Liu, J. Stehouwer, A. Jourabloo, Y. Atoum, and X. Liu, Presentation Attack Detection for Face in Mobile Phones, New York, USA: Springer International Publishing, 2019.

R. Shao, X. Lan, and P.C. Yuen, “Joint Discriminative Learning of Deep Dynamic Textures for 3D Mask Face Anti-Spoofing,” IEEE Trans. Inf. Forensics Secur., Vol. 14, No. 4, pp. 923–938, 2019.

H.E. Utami and H. Nugroho, “Face Spoof Detection by Motion Analysis on the Whole Video Frames,” Proc. 2017 5th Int. Conf. Instrumentation, Commun. Inf. Technol. Biomed. Eng. ICICI-BME 2017, 2017, pp. 213–218.

C.H. Yeh and H.H. Chang, “Face Liveness Detection Based on Perceptual Image Quality Assessment Features with Multi-scale Analysis,” Proc. - 2018 IEEE Winter Conf. Appl. Comput. Vision, WACV 2018, 2018, pp. 49–56.

B. Chen, W. Yang, and S. Wang, “Face Anti-Spoofing by Fusing High and Low Frequency Features for Advanced Generalization Capability,” Proc. - 3rd Int. Conf. Multimed. Inf. Process. Retrieval, MIPR 2020, 2020, pp. 199–204.

P. Zhang, F. Zou, Z. Wu, N. Dai, S. Mark, M. Fu, J. Zhao, and K. Li, “FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-Spoofing,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., 2019, pp. 1574–1583.

Z. Boulkenafet, J. Komulainen, L. Li, X. Feng, and A. Hadid, “OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations,” Proc. - 12th IEEE Int. Conf. Autom. Face Gesture Recognition, 2017, pp. 612–618.

Y. Liu, A. Jourabloo, and X. Liu, “Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2018, pp. 389–398.

Y. Liu, J. Stehouwer, A. Jourabloo, and X. Liu, “Deep Tree Learning for Zero-Shot Face Anti-Spoofing,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2019, pp. 4675–4684.

S. Zhang, A. Liu, J. Wan, Y. Liang, G. Guo, S. Escalera, H. Escalante, and S. Li, “CASIA-SURF: A Large-Scale Multimodal Benchmark for Face Anti-Spoofing,” IEEE Trans. Biometrics, Behav. Identity Sci., Vol. 2, No. 2, pp. 182–193, Apr. 2020.

A. Liu, Z. Tan, X. Li, J. Wan, S. Escalera, G. Guo, and S. Li, “CASIA-SURF CeFA: A Benchmark for Multimodal Cross-Ethnicity Face Anti-Spoofing,” arXiv:2003.05136, pp. 1–17, 2020.

Y. Zhang, Z. Yin, Y. Li, G. Yin, J. Yan, J. Shao, and Z. Liu, CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations, ser. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Vol. 12357 LNCS, pp. 70–85, 2020.

A. George, Z. Mostaani, D. Geissenbuhler, O. Nikisins, A. Anjos, and S. Marcel, “Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network,” IEEE Trans. Inf. Forensics Secur., Vol. 15, No. 1, pp. 42–55, 2020.

G. Heusch, A. George, D. Geissbühler, Z. Mostaani, and S. Marcel, “Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks,” arXiv:2007.11469, Vol. 14, No. 8, pp. 1–12, 2020.

Y. Liu, J. Stehouwer, and X. Liu, “On Disentangling Spoof Trace for Generic Face Anti-Spoofing,” arXiv:2007.09273, pp. 1-17, 2020.

A. George and S. Marcel, “Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks,” IEEE Trans. Inf. Forensics Secur., Vol. 16, pp. 361–375, 2021.

Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, “Searching Central Difference Convolutional Networks for Face Anti-Spoofing,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2020, pp. 5294–5304.

Z. Wang, Z. Yu, C. Zhao, X. Zhu, Y. Qin, Q. Zhou, F. Zhou, and Z. Lei, “Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2020, pp. 5041–5050.

X. Tu, Z. Ma, J. Zhao, G. Du, M. Xie, and J. Feng, “Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing,” ACM Trans. on Intel. Sys. and Technol., Vol. 11, No. 5, pp. 1-19, 2020.



DOI: https://doi.org/10.22146/ijitee.61827

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