Face Anti-Spoofing Scheme Using Handcraft Based and Deep Learning Methods

Malicious parties which impersonate systems by fake identities affect recognition performance of biometric systems. This study focuses on a strength anti-spoofing scheme based on decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involves consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this context, convolutional neural network (CNN) and Log-Gabor filter methods are used to learn deep representations and extract facial features of images respectively. In order to improve the robustness of proposed anti-spoofing framework, fusion of Log-Gabor and CNN methods is considered by applying decision-level-fusion technique. Finally, the performance of proposed anti- spoofing scheme is examined on public spoof databases such as Print-Attack and Replay-Attack face databases to detect fake facial images.

El Yapımı Tabanlı ve Derin Öğrenme Yöntemlerini Kullanan Yüz Yanıltma Önleme Şeması

Sahte kimliklerle sistemleri taklit eden kötü niyetli kişiler, biyometrik sistemlerin tanınma performansını etkilemektedir. Bu çalışma, bireyleri gerçek ve sahte terimlerle izlemek için karar düzeyinde füzyona dayalı güçlü bir sahtekarlık önleme şemasına odaklanmaktadır. Önerilen sahte tespit şeması, gerçek ve sahte bireyleri ayırt etmek için yüz görüntülerinde hem el yapımı hem de derin öğrenme tekniklerinin dikkate alınmasını içerir. Bu bağlamda, evrişimsel sinir ağı (CNN) ve Log-Gabor filtre yöntemleri sırasıyla görüntülerin derin temsillerini öğrenmek ve görüntülerin yüz özelliklerini çıkarmak için kullanılmaktadır. Önerilen sahteciliği önleme çerçevesinin sağlamlığını geliştirmek için, Log-Gabor ve CNN yöntemlerinin füzyonu, karar seviyesinde füzyon tekniği uygulanarak değerlendirilmiştir. Son olarak, önerilen sahteciliği önleme planının performansı, sahte yüz görüntülerini tespit etmek için Print- Attack ve Replay-Attack gibi halka açık veri tabanlarında incelenmiştir.

PDF

___

1. Galbally, J., Marcel, S., Fierrez, J., 2014. Biometric Antispoofing Methods: A Survey in Face Recognition. IEEE Access, 2, 1530-1552.

2. Martinez-Diaz, M., Fierrez, J., Galbally, J., Ortega-Garcia, J., 2011. An Evaluation of Indirect Attacks and Countermeasures in Fingerprint Verification Systems. Pattern Recognition Letters, 32(12), 1643-1651.

3. Nguyen, D.T., Yoon, H.S., Pham, T.D., Park, K.R., 2017. Spoof Detection for Finger-vein Recognition System Using NIR Camera. Sensors, 17(10), 2261.

4. Menotti, D., Chiachia, G., Pinto, A., Schwartz, W.R., Pedrini, H., Falcao, A.X., Rocha, A., 2015. Deep Representations for Iris, Face, and Fingerprint Spoofing Detection. IEEE Transactions on Information Forensics and Security, 10(4), 864-879.

5. Ratha, N.K., Connell, J.H., Bolle, R.M., 2001. An Analysis of Minutiae Matching Strength. In International Conference on Audio-and Video- Based Biometric Person Authentication, Springer, Berlin, Heidelberg, 223-228.

6. Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N., Ho, A.T., 2015. Detection of Face Spoofing Using Visual Dynamics. IEEE Transactions on Information Forensics and Security, 10(4), 762-777.

7. Eskandari, M., Sharifi, O., 2018. Designing Efficient Spoof Detection Scheme for Face Biometric. In International Conference on Image and Signal Processing, Springer, Cham, 427-434.

8. Anjos, A., Marcel, S., 2011, October. Counter- measures to Photo Attacks in Face Recognition: a Public Database and a Baseline. In 2011 International Joint Conference on Biometrics (IJCB) IEEE. 1-7.

9. Gupta, P., Behera, S., Vatsa, M., Singh, R., 2014. On Iris Spoofing Using Print Attack. In 2014 22nd International Conference on Pattern Recognition, IEEE, 1681-1686.

10. Hadid, A., Ghahramani, M., Kellokumpu, V., Pietikäinen, M., Bustard, J., Nixon, M., 2012. Can Gait Biometrics be Spoofed?. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), IEEE, 3280-3283.

11. Biggio, B., Akhtar, Z., Fumera, G., Marcialis, G.L., Roli, F., 2012. Security Evaluation of Biometric Authentication Systems Under Real Spoofing Attacks. IET biometrics, 1(1), 11-24.

12. Gomez-Barrero, M., Galbally, J., Fierrez, J., 2014. Efficient Software Attack to Multimodal Biometric Systems and its Application to Face and Iris Fusion. Pattern Recognition Letters, 36, 243-253.

13. Akhtar, Z., Kale, S., Alfarid, N., 2011. Spoof Attacks on Multimodal Biometric Systems. In International Conference on Information and Network Technology, 4, 46-51.

14. Chakka, M.M., Anjos, A., Marcel, S., Tronci, R., Muntoni, D., Fadda, G., Pili, M., Sirena, N., Murgia, G., Ristori, M., Roli, F., 2011. Competition on Counter Measures to 2-d Facial Spoofing Attacks. In 2011 International Joint Conference on Biometrics (IJCB), IEEE, 1-6.

15. Kollreider, K., Fronthaler, H., Bigun, J., 2005. Evaluating Liveness by Face Images and the Structure Tensor. In Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05), IEEE, 75-80.

16. Kollreider, K., Fronthaler, H., Bigun, J., 2008. Verifying Liveness by Multiple Experts in Face Biometrics. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 1-6.

17. Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. Imagenet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, 1097-1105.

18. Druzhkov, P.N., Kustikova, V.D., 2016. A Survey of Deep Learning Methods and Software Tools for Image Classification and Object Detection. Pattern Recognition and Image Analysis, 26(1), 9-15.

19. Eskandari, M., Toygar, Ö., 2015. Selection of Optimized Features and Weights on Face-iris Fusion Using Distance Images. Computer Vision and Image Understanding, 137, 63-75.

20. Print Attack face database, 2014. https://www.idiap.ch/dataset/printattack, Accessed October 2014.

21. Replay Attack face database, 2014, https://www.idiap.ch/dataset/replayattack, Accessed October 2014.

22. Galbally, J., Marcel, S., Fierrez, J., 2013. Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition. IEEE Transactions on Image Processing, 23(2), 710-724.

23. Bharadwaj, S., Dhamecha, T.I., Vatsa, M., Singh, R., 2013. Computationally Efficient Face Spoofing Detection with Motion Magnification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 105-110.

24. Määttä, J., Hadid, A., Pietikäinen, M., 2011. Face Spoofing Detection from Single Images Using Micro-texture Analysis. In 2011 International Joint Conference on Biometrics (IJCB), IEEE, 1-7.

25. de Freitas Pereira, T., Komulainen, J., Anjos, A., De Martino, J.M., Hadid, A., Pietikäinen, M., Marcel, S., 2014. Face Liveness Detection Using Dynamic Texture. EURASIP Journal on Image and Video Processing, 2014(1), 2.

26. Wen, D., Han, H., Jain, A.K., 2015. Face Spoof Detection with Image Distortion Analysis. IEEE Transactions on Information Forensics and Security, 10(4), 746-761.

27. Nguyen, D.T., Pham, T.D., Baek, N.R., Park, K.R., 2018. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-light Camera Sensors. Sensors, 18(3), 699.

28. Ojala, T., Pietikäinen, M., Harwood, D., 1996. A Comparative Study of Texture Measures with Classification Based on Featured Distributions. Pattern Recognition, 29(1), 51-59.

29. Benlamoudi, A., Samai, D., Ouafi, A., Bekhouche, S.E., Taleb-Ahmed, A., Hadid, A., 2015, May. Face Spoofing Detection Using Local Binary Patterns and Fisher Score. In 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), IEEE, 1-5.

30. De Souza, G.B., da Silva Santos, D.F., Pires, R.G., Marana, A.N., Papa, J.P., 2017. Deep Texture Features for Robust Face Spoofing Detection. IEEE Transactions on Circuits and Systems II: Express Briefs, 64(12), 1397-1401.

31. Alotaibi, A., Mahmood, A., 2017. Deep Face Liveness Detection Based on Nonlinear Diffusion Using Convolution Neural Network. Signal, Image and Video Processing, 11(4), 713-720.

32. Sajjad, M., Khan, S., Hussain, T., Muhammad, K., Sangaiah, A.K., Castiglione, A., Esposito, C., Baik, S.W., 2019. CNN-based Anti- spoofing Two-tier Multi-factor Authentication System. Pattern Recognition Letters, 126, 123-131.

33. Xu, Z., Li, S., Deng, W., 2015. November. Learning Temporal Features Using LSTM- CNN Architecture for Face Anti-spoofing. In 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), IEEE, 141-145.

34. Sharifi, O., 2019. Score-level-based Face Anti- Spoofing System Using Handcrafted and Deep Learned Characteristics. International Journal of Image, Graphics and Signal Processing, 11(2), 15.

35. Field, D.J., 1987. Relations Between the Statistics of Natural Images and the Response Properties of Cortical Cells. Josa a, 4(12), 2379-2394.

36. Ammour, B., Bouden, T., Boubchir, L., 2018. Face–iris Multi-modal Biometric System Using Multi-resolution Log-gabor Filter with Spectral Regression Kernel Discriminant Analysis. IET Biometrics, 7(5), 482-489.

37. Du, Y., 2006. Using 2D log-Gabor Spatial Filters for Iris Recognition. In Biometric Technology for Human Identification III,. International Society for Optics and Photonics, 6202, 62020F

38. Bounneche, M.D., Boubchir, L., Bouridane, A., Nekhoul, B., Ali-Chérif, A., 2016. Multi- spectral Palmprint Recognition Based on Oriented Multiscale log-Gabor Filters. Neurocomputing, 205, 274-286.

39. Simonyan, K., Zisserman, A., 2014. Very Deep Convolutional Networks for Large-scale Image Recognition. arXiv Preprint arXiv:1409.1556.