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Face Liveness Detection using Local Diffused Patterns

by Gautam Pallavi, Jayash Kumar Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 149 - Number 4
Year of Publication: 2016
Authors: Gautam Pallavi, Jayash Kumar Sharma
10.5120/ijca2016911380

Gautam Pallavi, Jayash Kumar Sharma . Face Liveness Detection using Local Diffused Patterns. International Journal of Computer Applications. 149, 4 ( Sep 2016), 1-5. DOI=10.5120/ijca2016911380

@article{ 10.5120/ijca2016911380,
author = { Gautam Pallavi, Jayash Kumar Sharma },
title = { Face Liveness Detection using Local Diffused Patterns },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 149 },
number = { 4 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume149/number4/25982-2016911380/ },
doi = { 10.5120/ijca2016911380 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:53:46.874171+05:30
%A Gautam Pallavi
%A Jayash Kumar Sharma
%T Face Liveness Detection using Local Diffused Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 149
%N 4
%P 1-5
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In general, face spoofing attacks deals with printing artifacts, electronic screens and ultra-realistic face masks or models. This paper proposes a liveness detection method based on diffusion speed. Diffusion speed of a single image is calculated as the difference of the original images and diffused images at each pixel. Face spoofing method based on diffusion speed does not require any user involvement and works with a single image. The key aspect of the proposed method is based on the difference in the illumination characteristics of live and fake faces. To solve the nonlinear, scalar valued diffusion equation, AOS (Additive Operator Splitting) approach, together with TDMA (Tri-Diagonal Matrix Algorithm) is applied. The local pattern of the diffusion speed is calculated at each pixel position (Local Diffused Patterns) and fed to linear Support Vector Machine for classification. Proposed approach performs well against the diverse malicious attacks, face display media (screen / paper) & varying illuminations and gives 90.83% accuracy.

References
  1. J. Bai, T. Ng, X. Gao, and Y. Shi, “Is physics-based liveness detection truly possible with a single image?”, IEEE International Symposium on Circuits and Systems, 2010, pp. 3425–3428.
  2. X. Tan, Y. Li, J. Liu, and L. Jiang, “Face liveness detection from a single image with sparse low rank bilinear discriminative model”, Computer Vision ECCV 2010, vol. 6316, pp. 504–517, 2010.
  3. J. M¨a¨att¨a, A. Hadid, and M. Pietik¨ainen, “Face spoofing detection from single image using micro-texture analysis,” International Joint Conference on Biometrics (IJCB), 2011.
  4. G. Pan, L. Sun, Z. Wu, and S. Lao, “Eyeblink-based anti-spoofing in face recognition from a generic web camera,” IEEE 11thInternational Conference on Computer Vision, 2007, pp. 1–8.
  5. G. Pan, Z. Wu, and L. Sun, “Liveness detection for face recognition,” Recent Advances in Face Recognition, December 2008, pp. 109–124.
  6. G. Pan, L. Sun, Z. Wu, and Y. Wang, “Monocular camera-based face liveness detection by combining eyeblink and scene context” Journal of Telecommunication Systems, 2009.
  7. W. Bao, H. Li, N. Li, and W. Jiang, “A liveness detection method for face recognition based on optical flow field,” IEEE International Conference on Image Analysis and Signal Processing, 2009, pp. 233–236.
  8. K. Kollreider, H. Fronthaler, J. Bigun, “Non-intrusive liveness detection by face images” Image and Vision Computing, 2009, vol. 27, no. 3, pp. 233–244.
  9. A. Anjos, M. M. Chakka, and S. Marcel, “Motion-based counter measures to photo attacks in face recognition” IET Biometrics, vol. 3, no. 3, pp. 147-158, September 2014.
  10. Santosh Tirunagari, Norman Poh, David Windridge, Aamo Iorliam, Nick Suki, Anthony T. S. Ho, “Detection of face spoofing using visual dynamics” IEEE Transaction on Information Forensics and Security”, vol. 10, No. 4, April 2015.
  11. Won Jun Kim, Sungjoo Suh, Jae Joon Han, “face liveness detection from single image via diffusion speed model” IEEE Transaction on Image Processing, vol. 24, No. 8, August 2015.
  12. R. Raghavendra, Kiran. B. Raju, Christoph Bush, “Presentation attack detection for face recognition using Light field camera", IEEE Transaction on Image Processing, vol. 24, No. 3, March 2015
  13. J. Ralli “PDE Based Image Diffusion and AOS”, PhD thesis, 2014.
  14. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, July 1990.
  15. J. Weickert, B.M.T.H. Romeny, and M.A. Viergever, “Efficient and reliable schemes for nonlinear diffusion filtering” IEEE Transaction on Image Processing, vol. 7, no. 3, pp. 398–410, Mar. 1998.
Index Terms

Computer Science
Information Sciences

Keywords

Spoofing diffusion speed local diffused pattern face liveness detection Additive Operator Splitting Tri-Diagonal Matrix Algorithm

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