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Face Anti-spoofing Based on Motion

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Advances in Multimedia Information Processing – PCM 2017 (PCM 2017)

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Abstract

People can have access to biometric system easily by face spoofing attack. Recent researches have proposed many anti-spoofing strategies based on eye blinking, facial expression changes, mouth movements or skin texture. As for the face which has slight trembling, there are few specific methods about it. To solve this problem, we have proposed a method which could discriminate human real face and face print by using parameters of slight face motion and equal-proportion property in projection. In order to validate our method, we also established a new video database containing 720 moving faces. After getting the facial landmarks, aligning image of each frame and calculating the variance as feature value, final data would be sent to the support vector machine (SVM) to verify the reality of faces. From the experiment results, the proposed method shows its high accuracy in different lighting conditions and different face amplitude for anti-spoofing and will have good prospect in engineer.

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Notes

  1. 1.

    https://github.com/seetaface/SeetaFaceEngine.

References

  1. 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

    Chapter  Google Scholar 

  2. Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer, USA (2008)

    Book  Google Scholar 

  3. Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: Biometrics Special Interest Group, pp. 1–7. IEEE (2012)

    Google Scholar 

  4. Galbally, J., Marcel, S.: Face anti-spoofing based on general image quality assessment. In: International Conference on Pattern Recognition, pp. 1173–1178. IEEE (2014)

    Google Scholar 

  5. Erdogmus, N., Marcel, S.: Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect. In: IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems, pp. 1–6. IEEE (2013)

    Google Scholar 

  6. Maatta, J., Hadid, A., Pietikainen, M.: Face spoofing detection from single images using micro-texture analysis. In: International Joint Conference on Biometrics, pp. 1–7. IEEE Computer Society (2011)

    Google Scholar 

  7. Anjos, A., Marcel, S.: Counter-measures to photo attacks in face recognition: a public database and a baseline. In: International Joint Conference on Biometrics, pp. 1–7. IEEE Computer Society (2011)

    Google Scholar 

  8. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, pp. 119–131. Wiley, New York (2001)

    MATH  Google Scholar 

  9. Pan, G., Sun, L., Wu, Z., et al.: Monocular camera-based face liveness detection by combining eyeblink and scene context. Telecommun. Syst. 47(3–4), 215–225 (2011)

    Article  Google Scholar 

  10. Pan, G., Wu, Z., Sun, L.: Liveness detection for face recognition. In: Recent Advances in Face Recognition. InTech (2008)

    Google Scholar 

  11. Tronci, R., Muntoni, D., Fadda, G., et al.: Fusion of multiple clues for photo-attack detection in face recognition systems. In: International Joint Conference on Biometrics, pp. 1–6. IEEE Computer Society (2011)

    Google Scholar 

  12. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. In: European Conference on Computer Vision, pp. 404–420. Springer-Verlag (2000)

    Google Scholar 

  13. Parveen, S., Ahmed, S., Mumtazah, S., et al.: Texture analysis using local ternary pattern for face anti-spoofing. Sci. Int. 28(2), 968–970 (2016)

    Google Scholar 

  14. Kollreider, K., Fronthaler, H., Bigun, J.: Non-intrusive liveness detection by face images. Image Vis. Comput. 27(3), 233–244 (2009)

    Article  Google Scholar 

  15. Bao, W., Li, H., Li, N., et al.: A liveness detection method for face recognition based on optical flow field. In: International Conference on Image Analysis and Signal Processing, pp. 233–236. IEEE (2009)

    Google Scholar 

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Acknowledgements

This work was partly supported by Hubei Province Technological Innovation Major Project (No. 2016AAA015), the National Nature Science Foundation of China (61502348), the EU FP7 QUICK project under Grant Agreement No. PIRSES-GA-2013-612652, and the science and technology program of Shenzhen (JCYJ20150422150029092).

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Correspondence to Jing Xiao .

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Wang, R., Xiao, J., Hu, R., Wang, X. (2018). Face Anti-spoofing Based on Motion. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-77383-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77382-7

  • Online ISBN: 978-3-319-77383-4

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