Abstract
As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Though promising progress has been achieved, existing works still have difficulty in handling complex spoof attacks and generalizing to real-world scenarios. The main reason is that current face anti-spoofing datasets are limited in both quantity and diversity. To overcome these obstacles, we contribute a large-scale face anti-spoofing dataset, CelebA-Spoof, with the following appealing properties: 1) Quantity: CelebA-Spoof comprises of 625,537 pictures of 10,177 subjects, significantly larger than the existing datasets. 2) Diversity: The spoof images are captured from 8 scenes (2 environments * 4 illumination conditions) with more than 10 sensors. 3) Annotation Richness: CelebA-Spoof contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset. Equipped with CelebA-Spoof, we carefully benchmark existing methods in a unified multi-task framework, Auxiliary Information Embedding Network (AENet), and reveal several valuable observations. Our key insight is that, compared with the commonly-used binary supervision or mid-level geometric representations, rich semantic annotations as auxiliary tasks can greatly boost the performance and generalizability of face anti-spoofing across a wide range of spoof attacks. Through comprehensive studies, we show that CelebA-Spoof serves as an effective training data source. Models trained on CelebA-Spoof (without fine-tuning) exhibit state-of-the-art performance on standard benchmarks such as CASIA-MFSD. The datasets are available at https://github.com/Davidzhangyuanhan/CelebA-Spoof.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that we do not learn environments \(\mathcal {S}^{\text {e}}\) since we take face image as input where environment cues (i.e. indoor or outdoor) cannot provide more valuable information yet illumination influences much.
- 2.
Please refer to supplementary for the detailed input sensors information.
References
Bhattacharjee, S., Mohammadi, A., Marcel, S.: Spoofing deep face recognition with custom silicone masks. In: Proceedings of IEEE 9th International Conference on Biometrics: Theory, Applications, and Systems (BTAS) (2018)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face antispoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Process. Lett. 24(2), 141–145 (2016)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. TIFS 11(8), 1818–1830 (2016)
Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: Oulu-npu: a mobile face presentation attack database with real-world variations. In: FG, pp. 612–618. IEEE (2017)
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: BIOSIG, pp. 1–7. IEEE (2012)
Chingovska, I., Erdogmus, N., Anjos, A., Marcel, S.: Face recognition systems under spoofing attacks. In: Bourlai, T. (ed.) Face Recognition Across the Imaging Spectrum, pp. 165–194. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28501-6_8
Erdogmus, N., Marcel, S.: Spoofing 2D face recognition systems with 3D masks. In: BIOSIG, pp. 1–8. IEEE (2013)
Feng, L., Po, L.M., Li, Y., Xu, X., Yuan, F., Cheung, T.C.H., Cheung, K.W.: Integration of image quality and motion cues for face anti-spoofing: a neural network approach. J. Visual Commun. Image Represent. 38, 451–460 (2016)
Feng, Y., Wu, F., Shao, X., Wang, Y., Zhou, X.: Joint 3D face reconstruction and dense alignment with position map regression network. In: ECCV, pp. 534–551 (2018)
Jourabloo, A., Liu, Y., Liu, X.: Face de-spoofing: anti-spoofing via noise modeling. In: ECCV, pp. 290–306 (2018)
Kim, T., Kim, Y., Kim, I., Kim, D.: BASN: enriching feature representation using bipartite auxiliary supervisions for face anti-spoofing. In: ICCV Workshops (2019)
Kollreider, K., Fronthaler, H., Faraj, M.I., Bigun, J.: Real-time face detection and motion analysis with application in “liveness” assessment. TIFS 2(3), 548–558 (2007)
Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. In: Biometric Technology for Human Identification, vol. 5404, pp. 296–303. International Society for Optics and Photonics (2004)
Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: IPTA, pp. 1–6. IEEE (2016)
Liu, S.Q., Lan, X., Yuen, P.C.: Remote photoplethysmography correspondence feature for 3D mask face presentation attack detection. In: ECCV, September 2018
Liu, S., Yang, B., Yuen, P.C., Zhao, G.: A 3D mask face anti-spoofing database with real world variations. In: CVPR Workshops, pp. 1551–1557, June 2016
Liu, S., Yuen, P.C., Zhang, S., Zhao, G.: 3D mask face anti-spoofing with remote photoplethysmography. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 85–100. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_6
Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: CVPR, pp. 389–398 (2018)
Liu, Y., Stehouwer, J., Jourabloo, A., Liu, X.: Deep tree learning for zero-shot face anti-spoofing. In: CVPR, pp. 4680–4689 (2019)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: ICCV (2015)
Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using texture and local shape analysis. IET Biom. 1(1), 3–10 (2012)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPAMI 24(7), 971–987 (2002)
Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: ICCV, pp. 1–8. IEEE (2007)
Patel, K., Han, H., Jain, A.K.: Secure face unlock: spoof detection on smartphones. TIFS 11(10), 2268–2283 (2016)
Schwartz, W.R., Rocha, A., Pedrini, H.: Face spoofing detection through partial least squares and low-level descriptors. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–8. IEEE (2011)
Shao, R., Lan, X., Li, J., Yuen, P.C.: Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In: CVPR (2019)
Sun, L., Pan, G., Wu, Z., Lao, S.: Blinking-based live face detection using conditional random fields. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 252–260. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_27
Wang, Z., et al.: Exploiting temporal and depth information for multi-frame face anti-spoofing. arXiv (2018)
Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. TIFS 10(4), 746–761 (2015)
Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection with component dependent descriptor. In: ICB, pp. 1–6. IEEE (2013)
Yang, X., et al.: Face anti-spoofing: model matters, so does data. In: CVPR, pp. 3507–3516 (2019)
Zhang, S., et al.: A dataset and benchmark for large-scale multi-modal face anti-spoofing. In: CVPR, pp. 919–928 (2018)
Zhang, X., Ng, R., Chen, Q.: Single image reflection separation with perceptual losses. In: ICCV, pp. 4786–4794 (2018)
Zhang, Z., et al.: A face antispoofing database with diverse attacks. In: ICB, pp. 26–31. IEEE (2012)
Acknowledgments
This work is supported in part by SenseTime Group Limited, in part by National Science Foundation of China Grant No. U1934220 and 61790575, and the project “Safety data acquisition equipment for industrial enterprises No.134”. The corresponding author is Jing Shao. The contributions of Yuanhan Zhang and Zhenfei Yin are Equal.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 2 (mp4 40583 KB)
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y. et al. (2020). CelebA-Spoof: Large-Scale Face Anti-spoofing Dataset with Rich Annotations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12357. Springer, Cham. https://doi.org/10.1007/978-3-030-58610-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-58610-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58609-6
Online ISBN: 978-3-030-58610-2
eBook Packages: Computer ScienceComputer Science (R0)