Abstract
Face Anti-spoofing (FAS) has arisen as one of the essential issues in face recognition systems. The existing deep learning FAS methods have achieved outstanding performance, but most of them are too complex to be deployed in embedded devices. Therefore, a tiny single modality FAS method (Tiny-FASNet) is proposed. First, to reduce the complexity, the tiny module is presented to simulate fully convolution operations. Specifically, some intrinsic features extracted by convolution are used to generate more features through cheap linear transformations. Besides, a simplified streaming module is proposed to keep more spatial structure information for FAS task. All models are trained and tested on depth images. The proposed model achieves 0.0034(ACER), 0.9990(TPR@FPR = 10E–2), and 0.9860(TPR@FPR = 10E–3) on CASIA-SURF dataset only with 0.018M parameters and 12.25M FLOPS. Extensive evaluations in two publicly available datasets (CASIA-SURF and CASIA-SURF CeFA) demonstrate the effectiveness of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Heusch, G., George, A., Geissbuhler, D., Mostaani, Z., Marcel, S.: Deep models and shortwave infrared information to detect face presentation attacks. IEEE Trans. Biometrics Behav. Identity Sci. 2(4), 399–409 (2020)
Peixotom, B., Michelassi, C., Rocha, A.: Face liveness detection under bad illumination conditions. In: IEEE International Conference on Image Processing, pp. 3557–3560 (2011)
Komulainen, J., Hadid, A., Pietikainen, M.: Context based face anti-spoofing International. In: International Conference on Biometrics: Theory, Applications and Systems, pp. 1–8 (2013)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: IEEE International Conference on Image Processing, pp. 2636–2640 (2015)
Patel, K., Han, H., Jain, A.: Secure face unlock: spoof detection on smartphones. IEEE Trans. Inf. Forensics Secur. 11(10), 2268–2283 (2016)
Patel, K., Han, H., Jain, A.: Cross-database face anti-spoofing with robust feature representation. In: Chinese Conference on Biometric Recognition, pp. 611–619, (2016)
Li, L., Feng, X.Y., Boulkenafet, Z., Xia, Z.Q., Li, M.M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: International Conference on Image Processing Theory, tools and Applications, pp. 1–6 (2016)
Wang, Z.Z., et al.: Exploiting temporal and depth information for multi-frame face anti-spoofing (2019). arXiv: 1811.05118
Yang, X., et al.: Face anti-spoofing: model matters, so does data. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3502–3511 (2019)
Shen, T., Huang, Y.Y., Tong, Z.J.: FaceBagNet: bag-of-local-features model for multi-modal face anti-spoofing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1611–1616 (2019)
Yu, Z.T., et al.: Searching central difference convolutional networks for face anti-spoofing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5294–5304 (2020)
Li, C., Li, L., Xuan, S.X., Yang, J., Du, S.Y.: Face anti-spoofing algorithm using generative adversarial networks with hypercomplex wavelet. J. Xi’an Jiaotong Univ. (2014). http://kns.cnki.net/kcms/detail/61.1069.T.20201215.0923.002.html
Liu, Y.J., Jourabloo, A., Liu, X.M.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 389–398 (2018)
Yu, Z.T., et al.: Searching central difference convolutional networks for face anti-spoofing. In: IEEE International Conference on Computer Vision, pp. 5294–5304 (2020)
Zhang, P., et al.: FeatherNets: convolutional neural networks as light as feather for face anti-spoofing. In: European Conference on Computer Vision, pp. 1574–1583 (2019)
Han, K., et al.: GhostNet: more features from cheap operations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1577–1586 (2020)
Gao, S.H., Han, Q., Li, D., Cheng, M.M., Peng, P.: Representative batch normalization with feature calibration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–11 (2021)
Howard, A., et al.: MobileNet: efficient convolutional neural networks for mobile vision applications. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2017)
Sandler, M., Howard, A., Zhu, M.L., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Ma, N.N., Zhang, X.Y., Zheng, H., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: European Conference on Computer Vision, pp. 1–16 (2018)
Zhang, S., et al.: A dataset and benchmark for large-scale multi-modal face anti-spoofing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 919–928 (2019)
Liu, A., et al.: CASIA-SURF CeFA: a benchmark for multi-modal cross-ethnicity face anti-spoofing (2020). arXiv: 2003.05136v1
Chen, S., Liu, Y., Gao, X., Han, Z.: MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices. In: Chinese Conference On Biometric Recognition, pp. 428–438 (2018)
Acknowledgments
The work was supported in part by the National Natural Science Foundation of China (61866022, 61972016, 62032016), and the Beijing Natural Science Foundation (L191007).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, C., Chang, E., Liu, F., Xuan, S., Zhang, J., Wang, T. (2021). Tiny-FASNet: A Tiny Face Anti-spoofing Method Based on Tiny Module. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-88010-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88009-5
Online ISBN: 978-3-030-88010-1
eBook Packages: Computer ScienceComputer Science (R0)