Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Detection of Video-Based Face Spoofing Using LBP and Multiscale DCT

  • Conference paper
  • First Online:

Part of the book series:Lecture Notes in Computer Science ((LNSC,volume 10082))

Included in the following conference series:

Abstract

Despite the great deal of progress during the recent years, face spoofing detection is still a focus of attention. In this paper, an effective, simple and time-saving countermeasure against video-based face spoofing attacks based on LBP (Local Binary Patterns) and multiscale DCT (Discrete Cosine Transform) is proposed. Adopted as the low-level descriptors, LBP features are used to extract spatial information in each selected frame. Next, multiscale DCT is performed along the ordinate axis of the obtained LBP features to extract spatial information. Representing both spatial and temporal information, the obtained high-level descriptors (LBP-MDCT features) are finally fed into a SVM (Support Vector Machine) classifier to determine whether the input video is a facial attack or valid access. Compared with state of the art, the excellent experimental results of the proposed method on two benchmarking datasets (Replay-Attack and CASIA-FASD dataset) have demonstrated its effectiveness.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Galbally, J., Marcel, S., Fierrez, J.: Biometric antispoofing methods: a survey in face recognition. IEEE Access2, 1530–1552 (2014)

    Article  Google Scholar 

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

    Google Scholar 

  3. Galbally, J., Fierrez, J., Alonso-Fernandez, F., Martinez-Diaz, M.: Evaluation of direct attacks to fingerprint verification systems. Telecommun. Syst.47(3–4), 243–254 (2011)

    Article  Google Scholar 

  4. Mjaaland, B.B., Bours, P., Gligoroski, D.: Walk the walk: attacking gait biometrics by imitation. In: Burmester, M., Tsudik, G., Magliveras, S., Ilić, I. (eds.) ISC 2010. LNCS, vol. 6531, pp. 361–380. Springer, Heidelberg (2011). doi:10.1007/978-3-642-18178-8_31

    Chapter  Google Scholar 

  5. Chen, H., Valizadegan, H., Jackson, C., Soltysiak, S., Jain, A.K.: Fake hands: spoofing hand geometry systems. In: 2005 Biometrics Consortium Conference (BCC) (2005)

    Google Scholar 

  6. Bin, Q., Jian-Fei, P., Guang-Zhong, C., Ge-Guo, D.: The anti-spoofing study of vein identification system. In: International Conference on Computational Intelligence and Security (ICCIS), pp. 357–360 (2009)

    Google Scholar 

  7. Akhtar, Z., Fumera, G., Marcialis, G.L., Roli, F.: Evaluation of serial and parallel multibiometric systems under spoofing attacks. In: 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 283–288 (2012)

    Google Scholar 

  8. Tome, P., Vanoni, M., Marcel, S.: On the vulnerability of finger vein recognition to spoofing. In: International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–10 (2014)

    Google Scholar 

  9. 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). doi:10.1007/978-3-642-15567-3_37

    Chapter  Google Scholar 

  10. Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S.: A face antispoofing database with diverse attacks. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 26–31. IEEE Press (2012)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  13. Pinto, A., Schwartz, W.R., Pedrini, H., de Rezende Rocha, A.: Using visual rhythms for detecting video-based facial spoof attacks. IEEE Trans. Inf. Forensics Secur.10(5), 1025–1038 (2015)

    Article  Google Scholar 

  14. Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. Proc. SPIE5404, 296–303 (2004)

    Article  Google Scholar 

  15. 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. IEEE Press, Brussels (2011)

    Google Scholar 

  16. Maatta, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using texture and local shape analysis. IET Biometrics1(1), 3–10 (2012)

    Article  Google Scholar 

  17. Kose, N., Dugelay, J.L.: Classification of captured and recaptured images to detect photograph spoofing. In: 2012 International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, pp. 1027–1032 (2012)

    Google Scholar 

  18. Maatta, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE Press, Washington, DC (2011)

    Google Scholar 

  19. Nguyen, H.H., Nguyen-Son, H.-Q., Nguyen, T.D., Echizen, I.: Discriminating between computer-generated facial images and natural ones using smoothness property and local entropy. In: Shi, Y.-Q., Kim, H.J., Pérez-González, F., Echizen, I. (eds.) IWDW 2015. LNCS, vol. 9569, pp. 39–50. Springer, Heidelberg (2016). doi:10.1007/978-3-319-31960-5_4

    Chapter  Google Scholar 

  20. 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. LNCS, vol. 7728, pp. 121–132. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37410-4_11

    Chapter  Google Scholar 

  21. 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(4), 762–777 (2015)

    Article  Google Scholar 

  22. Arashloo, S.R., Kittler, J., Christmas, W.: Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features. IEEE Trans. Inf. Forensics Secur.10(11), 2396–2407 (2015)

    Article  Google Scholar 

  23. 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)

    Article MathSciNet  Google Scholar 

  24. da Silva Pinto, A., Pedrini, H., Schwartz, W., Rocha, A.: Video-based face spoofing detection through visual rhythm analysis. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, Ouro Preto, pp. 221–228 (2012)

    Google Scholar 

  25. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis.57(2), 137–154 (2004)

    Article  Google Scholar 

  26. Arashloo, S.R., Kittler, J.: Class-specific kernel fusion of multiple descriptors for face verification using multiscale binarised statistical image features. IEEE Trans. Inf. Forensics Secur.9(12), 2100–2109 (2014)

    Article  Google Scholar 

  27. Arashloo, S.R., Kittler, J.: Dynamic texture recognition using multiscale binarized statistical image features. IEEE Trans. Multimedia16(8), 2099–2109 (2014)

    Article  Google Scholar 

  28. Chan, C.H., Tahir, M.A., Kittler, J., Pietikainen, M.: Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors. IEEE Trans. Pattern Anal. Mach. Intell.35(5), 1164–1177 (2013)

    Article  Google Scholar 

  29. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn.20(3), 273–297 (1995)

    MATH  Google Scholar 

  30. de Freitas Pereira, T., Komulainen, J., Anjos, A., De Martino, J.M., Hadid, A., Pietikäinen, M., Marcel, S.: Face liveness detection using dynamic texture. EURASIP J. Image Video Process.2014(2), 1–15 (2014)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61272414) and the research funding of State Key Laboratory of Information Security (2016-MS-07).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Jinan University, Guangzhou, China

    Ye Tian & Shijun Xiang

  2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Shijun Xiang

Authors
  1. Ye Tian

    You can also search for this author inPubMed Google Scholar

  2. Shijun Xiang

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toShijun Xiang.

Editor information

Editors and Affiliations

  1. New Jersey Institute of Technology, Newark, New Jersey, USA

    Yun Qing Shi

  2. Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

  3. University of Vigo, Vigo, Spain

    Fernando Perez-Gonzalez

  4. Chinese Academy of Sciences, Beijing, China

    Feng Liu

Rights and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Tian, Y., Xiang, S. (2017). Detection of Video-Based Face Spoofing Using LBP and Multiscale DCT. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_2

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

[8]ページ先頭
©2009-2025 Movatter.jp