Abstract
A face-spoofing attack occurs when an imposter manipulates a face recognition and verification system to gain access as a legitimate user by presenting a 2D printed image or recorded video to the face sensor. This paper presents an efficient and non-intrusive method to counter face-spoofing attacks that uses a single image to detect spoofing attacks. We apply a nonlinear diffusion based on an additive operator splitting scheme. Additionally, we propose a specialized deep convolution neural network that can extract the discriminative and high-level features of the input diffused image to differentiate between a fake face and a real face. Our proposed method is both efficient and convenient compared with the previously implemented state-of-the-art methods described in the literature review. We achieved the highest reported accuracy of 99% on the widely used NUAA dataset. In addition, we tested our method on the Replay Attack dataset which consists of 1200 short videos of both real access and spoofing attacks. An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results. However, this result can be improved by applying a sparse autoencoder learning algorithm to obtain a more distinguishable diffused image.








Similar content being viewed by others
References
Ren, X., Wu, X.W.: A novel dynamic user authentication scheme. In: 2012 International Symposium on Communications and Information Technologies (ISCIT), pp. 713–717 (2012)
Wayman, J., Jain, A., Maltoni, D., Maio, D.: An Introduction to Biometric Authentication Systems. Springer, Berlin (2005)
De Marsico, M., Nappi, M., Riccio, D., Tortora, G.: Entropy-based template analysis in face biometric identification systems. Signal Image Video Process. 7, 493–505 (2013)
Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. In: Defense and Security. International Society for Optics and Photonics, pp. 296–303 (2004)
Tan, X., Li, Y., Liu, J., Jiang, L.: Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Computer Vision–ECCV 2010, pp. 504–517. Springer, Berlin (2010)
Peixoto, B., Michelassi, C., Rocha, A.: Face liveness detection under bad illumination conditions. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 3557–3560 (2011)
Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 26–31 (2012)
Maatta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using micro-texture analysis. In: International Joint Conference on Biometrics (IJCB), pp. 1–7 (2011)
Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: BIOSIG—Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–7 (2012)
Kim, W., Suh, S., Han, J.J.: Face liveness detection from a single image via diffusion speed model. IEEE Trans. Image Process. 24, 2456–2465 (2015)
Jianwei, Y., Zhen, L., Shengcai, L., Li, S.Z.: Face liveness detection with component dependent descriptor. In: International Conference on Biometrics (ICB), pp. 1–6 (2013)
Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007, pp. 1–8 (2007)
Singh, A.K., Joshi, P., Nandi, G.C.: Face recognition with liveness detection using eye and mouth movement. In: International Conference on Signal Propagation and Computer Technology (ICSPCT), pp. 592–597 (2014)
Kim, S., Yu, S., Kim, K., Ban, Y., Lee, S. : Face liveness detection using variable focusing. In: International Conference on Biometrics (ICB), pp. 1–6 (2013)
Bharadwaj, S., Dhamecha, T.I., Vatsa, M., Singh, R.: Computationally efficient face spoofing detection with motion magnification. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 105–110 (2013)
Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N., Ho, A.T.S.: Detection of Face Spoofing Using Visual Dynamics. IEEE Trans. Inf. Forensics Secur. 10, 762–777 (2015)
Witkin, A.P.: Scale-space filtering. ed: Google Patents (1987)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)
Nadernejad, E., Sharifzadeh, S., Forchhammer, S.: Using anisotropic diffusion equations in pixon domain for image de-noising. Signal Image Video Process. 7, 1113–1124 (2013)
Weickert, J., Romeny, B.T.H., Viergever, M.: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans. Image Process. 7, 398–410 (1998)
Ralli, J.: PDE based image diffusion and AOS (2014). http://jarnoralli.com/images/pdf/non_linear_image_diffusion_and_aos_ralli_2014.pdf
Land, E.H., McCann, J.: Lightness and retinex theory. JOSA 61, 1–11 (1971)
Jia, B., Feng, W., Zhu, M.: Obstacle detection in single images with deep neural networks. Signal Image Video Process. 10, 1–8 (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Le Cun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404. Morgan Kaufmann Publishers Inc. San Francisco, CA (1990)
Garcia, C., Delakis, M.: Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1408–1423 (2004)
Lawrence, S., Giles, C.L.: Ah Chung, T., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8, 98–113 (1997)
Fasel, B.: Robust face analysis using convolutional neural networks. In: Proceedings. 16th International Conference on Pattern Recognition, pp. 40–43 (2002)
Bengio, S., Mariéthoz, J.: A statistical significance test for person authentication. In: ODYSSEY04—The Speaker and Language Recognition Workshop (2004)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We have no conflicts of interest to disclose.
Rights and permissions
About this article
Cite this article
Alotaibi, A., Mahmood, A. Deep face liveness detection based on nonlinear diffusion using convolution neural network. SIViP 11, 713–720 (2017). https://doi.org/10.1007/s11760-016-1014-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-016-1014-2