doi: 10.17586/2226-1494-2017-17-4-702-710


S. S. Volkova, Y. N. Matveev

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Article in Russian

For citation: Volkova S.S., Matveev Yu.N. Convolutional neural networks for face anti-spoofing. Scientific and Technical Journal of Information Technologies, Mechanics and Optics , 2017, vol. 17, no. 4, pp. 702–710 (in Russian). doi: 10.17586/2226-1494-2017-17-4-702-710


Subject of Research. We propose the method for detecting an incident at face authentication when imposter tries to disguise himself as a real client. He tries to falsify the client's face by photo or video. Method. The face anti-spoofing method involves two successive steps. Obtaining facial features takes place at the first stage. Classification is performed at the second stage for making a decision if the real person or an imposter is in front of the camera. Facial features are extracted with the use of deep convolutional neural network. Classification is realized by support vector machine. One frame or a group of frames can become the input data for the method. Main Results. The proposed method for anti-spoofing gives the possibility to work with both real faces obtained with low quality and with fake faces displayed on high-resolution displays. It is confirmed by experiments on two available test datasets. Experiments show that the average value of the first and second kind errors on the test data does not exceed 9%, and the accuracy reaches values over 91%. Classification results are comparable with the best results shown when applying the other known techniques for detecting spoofing attacks on the same test bases. Practical Relevance. The proposed method can be applied to improve the quality of face authentication systems, as well as for the development of multimodal biometric systems.

Keywords: spoofing attack, biometrics, security, face, neural network

 1.     Matveev Y.N. Technologies of biometric identification of a person by voice and other modalities. Engineering Journal: Science and Innovation, 2012, no. 3, p. 46–61. (In Russian)
2.     Physical access control biometrics. Available at: (accessed 05.05.2017).
3.     Kukharev G.A., Kamenskaya E.I., Matveev Y.N., Shchegoleva N.L. Methods for Face Image Processing and Recognition in Biometric Applications / Ed. M.V. Khitrov. St. Petersburg, Politekhnika Publ., 2013, 388 p.
4.     Li S.Z., Jain A.K. Handbook of Face Recognition. London, Springer-Verlag, 2011, 724 p. doi: 10.1007/978-0-85729-932-1
5.     De Marsico M., Nappi M., Tistarelli M. Face Recognition in Adverse Conditions. IGI Global, 2014, 480 p. doi: 10.4018/978-1-4666-5966-7
6.     Bourlai T. Face Recognition Across the Imaging Spectrum. Springer, 2016, 383 p. doi: 10.1007/978-3-319-28501-6
7.     Datta A.K., Datta M., Banerjee P.K. Face Detection and Recognition: Theory and Practice. Chapman and Hall/CRC, 2015, 326 p. doi: 10.1201/b19349
8.     Ojala T., Pietikainen M., Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, vol. 24, no. 7, pp. 971–987. doi: 10.1109/TPAMI.2002.1017623
9.     Shyama V.S., Mary Linda P.A. A survey on facial spoofing detection. International Journal of Science, Engineering and Technology Research, 2016, vol. 5, no. 1, pp. 49–53.
10.  Marcel S., Nixon M.S., Li S.Z. Handbook of Biometric Anti-Spoofing: Trusted Biometrics under Spoofing Attacks. Springer, 2014, 281 p. doi: 10.1007/978-1-4471-6524-8
11.  Galbally J., Marcel S., Fierrez J. Biometric antispoofing methods: a survey in face recognition. IEEE Access,2014, vol. 2, pp. 1530–1552. doi: 10.1109/ACCESS.2014.2381273
12.  Parveen S., Syed Ahmad, S.M., Hanafi M., Wan Adnan W.A. Face anti-spoofing methods. Current Science, 2015, vol. 108, no. 8, pp. 1491–1500. doi: 10.18520/cs/v108/i8/1491-1500
13.  Kostylev N.M., Gorevoy A.V. The liveness detection module based on spectral reflection characteristics of facial skin. Engineering Journal: Science and Innovation, 2013, no. 9, pp. 47–60. (In Russian) doi: 10.18698/2308-6033-2013-9-925
14.  Lagorio A., Tisterelli M., Cadoni M. et. al. Liveness detection based on 3D face shape analysis. Proc. International Workshop on Biometrics and Forensics, IWBF. Lisbon, Portugal, 2013, art. 657310. doi: 10.1109/IWBF.2013.6547310
15.  Chakarborty S., Das D. An overview of face liveness detection. International Journal on Information Theory, 2014, vol. 3, no. 2, pp. 11–25.
16.  Bao W., Li H., Li n., Jiang W. A liveness detection method for face recognition based on optical flow field. Proc. Int. Conf. of Image Analysis and Signal Processing. Tiazhou, China, 2009, pp. 233–236. doi: 10.1109/IASP.2009.5054589.
17.  Kollreider K., Fronthaler M., Bigun J. Evaluating liveness by face images and structure tensor. Proc. 4th IEEE Workshop on Automatic Identification Advanced Technologies. Washington, USA, 2005, pp. 75–80. doi: 10.1109/AUTOID.2005.20
18.  Jee H.-K., Jung S.-U., Yoo J.-H. Liveness detection for embedded face recognition system. International Journal of Biomedical Sciences, 2006, vol. 1, no. 4, pp. 235.
19.  Deng G., Coo B., Miao J. et al. Liveness check algorithm based on eye movement model using SVM. Journal of Computer Aided Design and Computer Graphics, 2003, vol. 15, no. 7, pp. 853–857.
20.  Kollreider K., Fronthaler M., Faraj M.I., Bigun J. Real-time face detection and motion analysis with application in liveness assessment. IEEE Transactions on Information Forensics and Security, 2007, vol. 2, no. 3, pp. 548–558. doi: 10.1109/TIFS.2007.902037
21.  Kim G., Eum S., Suhr J.K. et al. Face liveness detection based on texture and frequency analyses. Proc. 5th IAPR Int. Conf. on Biometrics, ICB. New Deli, India, 2012, pp. 67–72. doi: 10.1109/ICB.2012.6199760
22.  Yang J., Lei Z., Liao S., Li S.Z. Face liveness detection with component dependent descriptor. Proc. Int. Conf. on Biometrics, ICB. Madrid, Spain, 2013. doi: 10.1109/ICB.2013.6612955
23.  Xiong X., De la Torre F. Supervised descent method and its applications to face alignment. Proc. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR. Portland, USA, 2013, pp. 532–539. doi: 10.1109/CVPR.2013.75
24.  Krizhevsky A., Sutskever I., Hinton G.E. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2012, vol. 2, pp. 1097–1105.
25.  Deng J., Dong W., Socher R., Li L., Li K., Fei-Fei L. ImageNet: a large-scale hierarchical image database. Proc. IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009. doi: 10.1109/cvprw.2009.5206848 
26.  LeCun Y., Bottou L., Orr G.B., Muller K.R. Efficient BackProp. Lecture Notes in Computer Science, 1998, vol. 1524, pp. 9–50. doi: 10.1007/3-540-49430-8_2.
27.  Zhang Z., Yan J., Liu S., Lei Z., Yi D., Li S.Z. A face antispoofing database with diverse attacks. Proc. 5th IAPR Int. Conf. on Biometrics, ICB. New Delhi, India, 2012, pp. 26–31. doi: 10.1109/ICB.2012.6199754.
28.  Zinelabidine B., Jukka K., Li L., Feng X., Hadid A. OULU-NPU: a mobile face presentation attack database with real-world variations. Proc. IEEE Int. Conf. on Identity, Security and Behavior Analysis, ISBA. New Delhi, India, 2017, pp. 1–7.
29.  Yi D., Lei Z., Liao S., Li S.Z. Learning Face Representation from Scratch. arXiv preprint, arXiv:1411.7923, 2014, 9 p.
30.  Jia Y., Shelhamer E., Donahue J., Karayev S., Long J., Girshick R., Guadarrama S., Darrel T. Caffe: convolutional architecture for fast feature embedding. Proc. ACM Conference on Multimedia. Orlando, USA, 2014, pp. 675–678.
31.  Tan X., Li Y., Liu J., Jiang L. Face liveness detection from a single image with sparse low rank bilinear discriminative model. Lecture Notes in Computer Science, 2010, vol. 6316, pp. 504–517. doi: 10.1007/978-3-642-15567-3_37
32.  Peixoto B., Michelassi C., Rocha A. Face liveness detection under bad illumination conditions. Proc. IEEE 18th Int. Conf. of Image Processing, ICIP. Brussels, Belgium, 2011, pp. 3557–3560 doi: 10.1109/ICIP.2011.6116484
33.  Maatta J., Hadid A., Pietik M. Face spoofing detection from single images using micro-texture analysis. Proc. 2011 Int. Joint Conference on Biometrics, IJCB. Washington, USA, 2011. doi: 10.1109/IJCB.2011.6117510.
34.  Kose N., Dugelay J.L. Classification of captured and recaptured images to detect photograph spoofing. Proc. Int. Conf. on Informatics, Electronics and Vision. Dhaka, India, 2012, pp. 1027–1032. doi: 10.1109/ICIEV.2012.6317336
35.  Chingovska I., Anjos A., Marcel S. On the effectiveness of local binary patterns in face anti-spoofing. Proc. Int. Conf. on Biometrics Special Interest Group, BIOSIG. Darmstadt, Germany, 2012.

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