Face Liveness Detection : An Overview

Authors

  • Shweta Policepatil  Department of Computer Science and Engineering Basaveshwar Engineering College (Autonomous), Bagalkot, Karnataka, India
  • Sanjeevakumar M. Hatture  Department of Computer Science and Engineering Basaveshwar Engineering College (Autonomous), Bagalkot, Karnataka, India

DOI:

https://doi.org//10.32628/IJSRST21843

Keywords:

Face Liveness Detection System, Machine Learning, Deep Learning, Face Detection, Face Spoofing.

Abstract

As the world becomes more and more digitized, the threat to security grows at an alarming rate. The mass usage of technology has garnered the attention and curiosity of people with foul intentions, whose aim is to exploit this use of technology to commit theft and other heinous crimes. One such technology used for security purposes is “Facial Recognition”. Face recognition is a popular biometric technique. Face recognition technology has advanced fast in recent years, and when compared to other ways, it is more direct, user-friendly, and convenient. Face recognition systems, on the other hand, are vulnerable to spoof assaults by non-real faces. To protect against spoofing, a secure system requires liveness detection. This study examines researchers' attempts to address the problem of spoofing and liveness detection, including mapping the research overview from the literature survey into a suitable taxonomy, exploring the fundamental properties of the field, motivation for using liveness detection methods in face recognition, and problems that may limit the benefits.

References

  1. X. Zhu, S. Li, X. Zhang, H. Li and A. C. Kot, "Detection of Spoofing Medium Contours for Face Anti-Spoofing," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 5, pp. 2039-2045, May 2021, doi: 10.1109/TCSVT.2019.2949868.
  2. R. Cai, H. Li, S. Wang, C. Chen and A. C. Kot, "DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing," in IEEE Transactions on Information Forensics and Security, vol. 16, pp. 937-951, 2021, doi: 10.1109/TIFS.2020.3026553.
  3. W. Sun, Y. Song, C. Chen, J. Huang and A. C. Kot, "Face Spoofing Detection Based on Local Ternary Label Supervision in Fully Convolutional Networks," in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3181-3196, 2020, doi: 10.1109/TIFS.2020.2985530.
  4. W. Sun, Y. Song, H. Zhao and Z. Jin, "A Face Spoofing Detection Method Based on Domain Adaptation and Lossless Size Adaptation," in IEEE Access, vol. 8, pp. 66553-66563, 2020, doi: 10.1109/ACCESS.2020.2985453.
  5. Patil, Rashmi & Bellary, Sreepathi. (2020). Machine learning approach in melanoma cancer stage detection. Journal of King Saud University - Computer and Information Sciences. 10.1016/j.jksuci.2020.09.002.
  6. H. Chen, Y. Chen, X. Tian and R. Jiang, "A Cascade Face Spoofing Detector Based on Face Anti-Spoofing R-CNN and Improved Retinex LBP," in IEEE Access, vol. 7, pp. 170116-170133, 2019, doi: 10.1109/ACCESS.2019.2955383.
  7. R. Shao, X. Lan and P. C. Yuen, "Joint Discriminative Learning of Deep Dynamic Textures for 3D Mask Face Anti-Spoofing," in IEEE Transactions on Information Forensics and Security, vol. 14, no. 4, pp. 923-938, April 2019, doi: 10.1109/TIFS.2018.2868230.
  8. A. Şengür, Z. Akhtar, Y. Akbulut, S. Ekici and Ü. Budak, "Deep Feature Extraction for Face Liveness Detection," 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1-4, doi: 10.1109/IDAP.2018.8620804.
  9. Alotaibi, A.; Mahmood, A. Deep face liveness detection based on nonlinear diffusion using convolutional neural network. SIViP 2017, 11, 713–720; doi:10.1007/s11760–016–1014–2.
  10. Boulkenafet, Zinelabidine & Komulainen, Jukka & Hadid, Abdenour. (2016). Face Spoofing Detection Using Colour Texture Analysis. IEEE Transactions on Information Forensics and Security. 11. 1-1. 10.1109/TIFS.2016.2555286.
  11. Parveen, S.; Ahmad, S.M.S.; Abbas, N.H.; Adnan, W.A.W.; Hanafi, M.; Naeem, N. Face Liveness Detection Using Dynamic Local Ternary Pattern (DLTP). Computers 2016, 5, 10.
  12. Y. Li, Y. Li, Q. Yan, H. Kong, and R. H. Deng, “Seeing Your Face Is Not Enough: An Inertial Sensor-Based Liveness Detection for Face Authentication,” in CCS 15: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 2015, pp. 1558–1569.
  13. Andrea Lagorio, Massimo Tistarelli, Marinella Cadoni, Liveness Detection based on 3D Face Shape Analysis, Biometrics and Forensics (IWBF), 2013 International Workshop on Page(s): 1-4, 2013
  14. Wang, T.; Yang, J.; Lei, Z.; Liao, S.; Li, S.Z. Face Liveness Detection Using 3D Structure recovered from a single camera. In proceedings of the 2013 International Conference on Biometrics (ICB), Madrid, Spain, 4– 7 June 2013; pp. 1–6, doi:10.1109/ICB.2013.6612957.
  15. G. Kim, S.Eum, J. K. Suhr, D. I. Kim, K. R. Park, and J, Kim, Face liveness detection based on texture and frequency analyses, 5th IAPR International Conference on Biometrics (ICB), New Delhi, India. pp. 67-72, March 2012
  16. Lin Sun, Gang Pan, Zhaohui Wu, Shihong Lao, Blinking-Based Live Face Detection Using Conditional Random Fields, ICB 2007, Seoul, Korea, International Conference, on pages 252-260, August 27-29, 2007.
  17. BG Nalinakshi, Sanjeevakumar M Hatture, Manjunath S Gabasavalgi, Rashmi P Karchi, “Liveness detection technique for prevention of spoof attack in face recognition system”, nternational Journal of Emerging Technology and Advanced Engineering, vol 3, issue 12, pages. 627-633, 2013.

International Journal of Scientific Research in Science and Technology

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Published

2021-07-30

Issue

Volume 8, Issue 4, July-August-2021

Section

Research Articles

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How to Cite

[1]
Shweta Policepatil, Sanjeevakumar M. Hatture, " Face Liveness Detection : An Overview, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 8, Issue 4, pp.22-29, July-August-2021. Available at doi : https://doi.org/10.32628/IJSRST21843