Abstract
Face liveness detection is an essential prerequisite for face recognition applications. Previous face liveness detection methods usually train a binary classifier to differentiate between a fake face and a real face before face recognition. The client identity information is not utilized in previous face liveness detection methods. However, in practical face recognition applications, face spoofing attacks are always aimed at a specific client, and the client identity information can provide useful clues for face liveness detection. In this paper, we propose a face liveness detection method based on the client identity using Siamese network. We detect face liveness after face recognition instead of before face recognition, that is, we detect face liveness with the client identity information. We train a Siamese network with image pairs. Each image pair consists of two real face images or one real and one fake face images. The face images in each pair come from a same client. Given a test face image, the face image is firstly recognized by face recognition system, then the real face image of the identified client is retrieved to help the face liveness detection. Experiment results demonstrate the effectiveness of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lei, L., Xia, Z., Li, L., Jiang, X., Roli, F.: Face anti-spoofing via hybrid convolutional neural network. In: 2017 International Conference on the Frontiers and Advances in Data Science (FADS) (2017)
Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face anti-spoofing using patch and depth-based CNNs. In: IEEE International Joint Conference on Biometrics (2018)
Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: International Joint Conference on Biometrics (2011)
Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. Proc. SPIE 5404, 296–303 (2004)
Das, D., Chakraborty, S.: Face liveness detection based on frequency and micro-texture analysis. In: International Conference on Advances in Engineering & Technology Research (2015)
Gang, P., Lin, S., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: IEEE International Conference on Computer Vision (2007)
Wei, B., Hong, L., Nan, L., Wei, J.: A liveness detection method for face recognition based on optical flow field. In: International Conference on Image Analysis & Signal Processing (2009)
Benlamoudi, A., Samai, D., Ouafi, A., Bekhouche, S.E., Talebahmed, A., Hadid, A.: Face spoofing detection using local binary patterns and fisher score, pp. 1–5 (2015)
Schwartz, W.R., Rocha, A., Pedrini, H.: Face spoofing detection through partial least squares and low-level descriptors. In: International Joint Conference on Biometrics (2011)
Bromley, J., Guyon, I., Lecun, Y., Säckinger, E., Shah, R.: Signatureverification using a “siamese” time delay neural network. Int. J. Pattern Recogn. Artif. Intell. 7(04), 669–688 (1993)
Chopra, S., Hadsell, R., Lecun, Y.: Learning a similarity metric discriminatively, with application to face verification, vol. 1, pp. 539–546 (2005)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Neural Information Processing Systems vol. 141, no. (5), pp. 1097–1105 (2012)
Bukovcikova, Z., Sopiak, D., Oravec, M., Pavlovicova, J.: Face verification using convolutional neural networks with Siamese architecture, pp. 205–208 (2017)
Tan, X., Li, Y., Liu, J., Jiang, L.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 504–517. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15567-3_37
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing, pp. 1–7 (2012)
Maatta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using micro-texture analysis. Int. J. Cent. Bank., 1–7 (2011)
Maatta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using texture and local shape analysis. IET Biometrics 1(1), 3–10 (2012)
Yuan, H., Li, S., Deng, H.: 2D face spoofing detection method based on multi-feature fusion. Comput. Appl. Softw. (2017)
Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network, pp. 1–6 (2016)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: International Conference on Image Processing, pp. 2636–2640 (2015)
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face antispoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Process. Lett. 24, 141–145 (2017)
Patel, K., Han, H., Jain, A.K., Ott, G.: Live face video vs. spoof face video: use of moiré patterns to detect replay video attacks, pp. 98–105 (2015)
Singh, A.K., Joshi, P., Nandi, G.C.: Face recognition with liveness detection using eye and mouth movement. In: 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014), pp. 592–597. IEEE (2014)
Alotaibi, A., Mahmood, A.: Deep face liveness detection based on nonlinear diffusion using convolution neural network. Signal Image Video Process. 11(4), 713–720 (2017)
Erdogmus, N., Marcel, S.: Spoofing 2D face recognition systems with 3D masks, pp. 1–8 (2013)
Hadsell, R., Chopra, S., Lecun, Y.: Dimensionality reduction by learning an invariant mapping, vol. 2, pp. 1735–1742 (2006)
Chingovska, I., Anjos, A.R.D.: On the use of client identity information for face antispoofing. IEEE Trans. Inf. Forensics Secur. 10(4), 787–796 (2017)
Arashloo, S.R., Kittler, J.: Client-specific anomaly detection for face presentation attack detection (2018)
Acknowledgement
This research was supported by China Postdoctoral Science Foundation Grant (2018M642680).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hao, H., Pei, M., Zhao, M. (2019). Face Liveness Detection Based on Client Identity Using Siamese Network. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-31654-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31653-2
Online ISBN: 978-3-030-31654-9
eBook Packages: Computer ScienceComputer Science (R0)