Advertisement

Face De-spoofing: Anti-spoofing via Noise Modeling

  • Amin Jourabloo
  • Yaojie LiuEmail author
  • Xiaoming Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11217)

Abstract

Many prior face anti-spoofing works develop discriminative models for recognizing the subtle differences between live and spoof faces. Those approaches often regard the image as an indivisible unit, and process it holistically, without explicit modeling of the spoofing process. In this work, motivated by the noise modeling and denoising algorithms, we identify a new problem of face de-spoofing, for the purpose of anti-spoofing: inversely decomposing a spoof face into a spoof noise and a live face, and then utilizing the spoof noise for classification. A CNN architecture with proper constraints and supervisions is proposed to overcome the problem of having no ground truth for the decomposition. We evaluate the proposed method on multiple face anti-spoofing databases. The results show promising improvements due to our spoof noise modeling. Moreover, the estimated spoof noise provides a visualization which helps to understand the added spoof noise by each spoof medium.

Keywords

Face anti-spoofing Generative model CNN Image decomposition 

Notes

Acknowledgment

This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2017-17020200004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

References

  1. 1.
    Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: ICCV. IEEE (2007)Google Scholar
  2. 2.
    Patel, K., Han, H., Jain, A.K.: Cross-database face antispoofing with robust feature representation. In: You, Z. (ed.) CCBR 2016. LNCS, vol. 9967, pp. 611–619. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46654-5_67CrossRefGoogle Scholar
  3. 3.
    de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: LBP-TOP based countermeasure against face spoofing attacks. In: Park, J.-I., Kim, J. (eds.) ACCV 2012, Part I. LNCS, vol. 7728, pp. 121–132. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37410-4_11CrossRefGoogle Scholar
  4. 4.
    de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: Can face anti-spoofing countermeasures work in a real world scenario? In: ICB. IEEE (2013)Google Scholar
  5. 5.
    Komulainen, J., Hadid, A., Pietikainen, M.: Context based face anti-spoofing. In: BTAS. IEEE (2013)Google Scholar
  6. 6.
    Yang, J., Lei, Z., Liao, S., Li, S.Z.: Face liveness detection with component dependent descriptor. In: ICB. IEEE (2013)Google Scholar
  7. 7.
    Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: 2016 6th International Conference on Image Processing Theory Tools and Applications (IPTA). IEEE (2016)Google Scholar
  8. 8.
    Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing (2014). arXiv preprint: arXiv:1408.5601
  9. 9.
    Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10593-2_13CrossRefGoogle Scholar
  10. 10.
    Jourabloo, A., Feghahati, A., Jamzad, M.: New algorithms for recovering highly corrupted images with impulse noise. Sci. Iranica 19(6), 1738–1745 (2012)CrossRefGoogle Scholar
  11. 11.
    Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., Ashok, A.: Reconnet: non-iterative reconstruction of images from compressively sensed measurements. In: CVPR. IEEE (2016)Google Scholar
  12. 12.
    Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: CVPR. IEEE (2016)Google Scholar
  13. 13.
    Lefkimmiatis, S.: Non-local color image denoising with convolutional neural networks. In: CVPR. IEEE (2017)Google Scholar
  14. 14.
    Tai, Y., Yang, J., Liu, X., Xu, C.: Memnet: a persistent memory network for image restoration. In: ICCV. IEEE (2017)Google Scholar
  15. 15.
    Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: CVPR. IEEE (2017)Google Scholar
  16. 16.
    Zhou, R., Achanta, R., Süsstrunk, S.: Deep residual network for joint demosaicing and super-resolution (2018). arXiv preprint: arXiv:1802.06573
  17. 17.
    Gharbi, M., Chaurasia, G., Paris, S., Durand, F.: Deep joint demosaicking and denoising. ACM Trans. Graph. 35(6), 191 (2016)CrossRefGoogle Scholar
  18. 18.
    Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: CVPR. IEEE (2018)Google Scholar
  19. 19.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
  20. 20.
    Jourabloo, A., Liu, X.: Pose-invariant face alignment via CNN-based dense 3D model fitting. Int. J. Comput. Vis. 124(2), 187–203 (2017)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: ICJB. IEEE (2011)Google Scholar
  22. 22.
    Patel, K., Han, H., Jain, A.K.: Secure face unlock: spoof detection on smartphones. IEEE Trans. Inf. Forens. Secur. 11(10), 2268–2283 (2016)CrossRefGoogle Scholar
  23. 23.
    Boulkenafet, Z., Komulainen, J., Hadid, A.: Face antispoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Process. Lett. 24(2), 141–145 (2017)Google Scholar
  24. 24.
    Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: ICIP. IEEE (2015)Google Scholar
  25. 25.
    Boulkenafet, Z., Komulainen, J., Hadid, A.: Face spoofing detection using colour texture analysis. IEEE Trans. Inf. Forens. Secur. 11(8), 1818–1830 (2016)CrossRefGoogle Scholar
  26. 26.
    Siddiqui, T.A., et al.: Face anti-spoofing with multifeature videolet aggregation. In: ICPR. IEEE (2016)Google Scholar
  27. 27.
    Bao, W., Li, H., Li, N., Jiang, W.: A liveness detection method for face recognition based on optical flow field. In: IASP. IEEE (2009)Google Scholar
  28. 28.
    Feng, L., et al.: Integration of image quality and motion cues for face anti-spoofing: a neural network approach. J. Vis. Commun. Image Represent. 38, 451–460 (2016)CrossRefGoogle Scholar
  29. 29.
    Xu, Z., Li, S., Deng, W.: Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In: IAPR Asian Conference. IEEE (2015)Google Scholar
  30. 30.
    Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. In: Biometric Technology for Human Identification. SPIE (2004)Google Scholar
  31. 31.
    Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face anti-spoofing using patch and depth-based CNNs. In: ICJB. IEEE (2017)Google Scholar
  32. 32.
    Tran, L., Yin, X., Liu, X.: Disentangled representation learning GAN for pose-invariant face recognition. In: CVPR. IEEE (2017)Google Scholar
  33. 33.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: CVPR. IEEE (2005)Google Scholar
  34. 34.
    Zhang, H., Sindagi, V., Patel, V.M.: Image de-raining using a conditional generative adversarial network (2017). arXiv preprint: arXiv:1701.05957
  35. 35.
    Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: CVPR. IEEE (2018)Google Scholar
  36. 36.
    Criminisi, A., Pérez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)CrossRefGoogle Scholar
  37. 37.
    Bertalmio, M., Vese, L., Sapiro, G., Osher, S.: Simultaneous structure and texture image inpainting. IEEE Trans. Image Process. 12(8), 882–889 (2003)CrossRefGoogle Scholar
  38. 38.
    Chen, Y., Tai, Y., Liu, X., Shen, C., Yang, J.: FSRNet: end-to-end learning face super-resolution with facial priors. In: CVPR. IEEE (2018)Google Scholar
  39. 39.
    Liu, Y., Shu, C.: A comparison of image inpainting techniques. In: Sixth International Conference on Graphic and Image Processing (ICGIP 2014). SPIE (2015)Google Scholar
  40. 40.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. IEEE (2016)Google Scholar
  41. 41.
    Liu, Y., Jourabloo, A., Ren, W., Liu, X.: Dense face alignment. In: ICCVW. IEEE (2017)Google Scholar
  42. 42.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part II. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46475-6_43CrossRefGoogle Scholar
  43. 43.
    Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: OULU-NPU: a mobile face presentation attack database with real-world variations. In: FG. IEEE (2017)Google Scholar
  44. 44.
    Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.Z.: A face antispoofing database with diverse attacks. In: ICB. IEEE (2012)Google Scholar
  45. 45.
    Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. IEEE (2012)Google Scholar
  46. 46.
    Abadi, M., Agarwal, A., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015)Google Scholar
  47. 47.
    ISO/IEC JTC 1/SC 37 Biometrics: Information technology biometric presentation attack detection part 1: Framework. International organization for standardization (2016). https://www.iso.org/obp/ui/iso
  48. 48.
    Bengio, S., Mariéthoz, J.: A statistical significance test for person authentication. In: Proceedings of Odyssey 2004: The Speaker and Language Recognition Workshop (2004)Google Scholar
  49. 49.
    Boulkenafet, Z.: A competition on generalized software-based face presentation attack detection in mobile scenarios. In: ICJB. IEEE (2017)Google Scholar
  50. 50.
    Bharadwaj, S., Dhamecha, T.I., Vatsa, M., Singh, R.: Computationally efficient face spoofing detection with motion magnification. In: CVPRW. IEEE (2013)Google Scholar
  51. 51.
    Pinto, A., Pedrini, H., Schwartz, W.R., Rocha, A.: Face spoofing detection through visual codebooks of spectral temporal cubes. IEEE Trans. Image Process. 24(12), 4726–4740 (2015)MathSciNetCrossRefGoogle Scholar
  52. 52.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA

Personalised recommendations