Lie Detection Using Facial Micro- Expresions (September 2020)

Authors

  • Ritom Tamuli  Information Science & Engineering, Bangalore, Karnataka, India
  • Srutibanta Samantara  Information Science & Engineering, Bangalore, Karnataka, India
  • Shubhodeep Sarkar  Information Science & Engineering, Bangalore, Karnataka, India
  • Sourav Adhikari  Information Science & Engineering, Bangalore, Karnataka, India

Keywords:

Lie detection, micro expressions, Emotions, Expressions

Abstract

In many fields, such as airport management, criminal inquiries, counterterrorism, etc., identifying lies is key. We can't go to the people in this area requesting for a practical lie detection test as it takes a hard task which takes a lot of time. An evergreen and changing topic has been Lie detection. The most common and effective approach to date has been polygraph techniques. The biggest downside of the polygraph is that it is difficult to obtain successful outcomes without establishing physical interaction with the person under investigation. This physical touch will usually trigger additional attention in the subject. One way of identifying lies is to recognize facial micro- expressions, which are small, spontaneous expressions seen on the face of individuals as they attempt to hide or repress emotions. So, our goal is to use facial micro- expressions to build and improve a lie detection system. The system's primary goal is to target the slight changes that occur in the face when someone is tricky and deceptive.

References

  1. S. Afzal and P. Robinson. Natural affect data- collection & annotation in a learning context. In ACII, pages 1–7, 2009. 2
  2. L. Breiman. Random forests. Machine Learning, 45(1):5– 32, 2001. 6
  3. T. Cootes, C. Taylor, D. Cooper, and J. Graham. Active shape models – their training and application. Computer Vision and Image Understanding, 61(1):38– 59, 1995. 3
  4. P. Ekman. Lie catching and microexpressions. The Philosophy of Deception, Oxford University Press, 2009. 1, 2, 5
  5. M. G. Frank, M. Herbasz, K. Sinuk, A. Keller, and
  6. C. Nolan. I see how you feel: Training laypeople and professionals to recognize fleeting emotions. International Communication Association, Sheraton New York City, 2009. 1, 2, 7
  7. A. Freitas-Magalh˜aes. The psychology of emotions: The allure of human face. Uni. Fernando Pessoa Press, 2007. 1
  8. A. Goshtasby. Image registration by local approximation methods. IMAVIS, 6(4):255–261, 1988. 3 [8] J. Gottman and R. Levenson. A two-factor model for predicting when a couple will divorce: Exploratory analyses using 14-year longitudinal data.
  9. Family process, 41(1):83–96, 2002. 1, 2
  10. X. He, D. Cai, S. Yan, and H. Zhang. Neighborhood preserving embedding. In ICCV, pages 1208–1213, 2005. 5
  11. S. Koelstra, M. Pantic, and I. Patras. A dynamic texture based approach to recognition of facial actions and their temporal models. PAMI, 32(11):1940–1954, 2010. 2
  12. N. Michael, M. Dilsizian, D. Metaxas, and J. Burgoon. Motion profiles for deception detection using visual cues. In ECCV, pages 462–475, 2010. 2
  13. T. Ojala, M. Pietik¨ainen, and T. M¨aenp¨a¨a. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI, 24(7):971–987, 2002. 4
  14. S. Polikovsky, Y. Kameda, and Y. Ohta. Facial microexpressions recognition using high speed camera and 3Dgradient descriptor. In ICDP, 2009. 1, 2
  15. M. Shreve, S. Godavarthy, D. Goldgof, and S. Sarkar. Macroand micro-expression spotting in long videos using spatiotemporal strain. In FG, 2011. 1, 2
  16. M. Shreve, S. Godavarthy, V. Manohar, D. Goldgof, and S. Sarkar. Towards macro- and micro- expression spotting in video using strain patterns. In Workshop on Applications of Computer Vision, pages 1–6, 2010. 2
  17. M. Varma and D. Ray. Learning the discriminative powerinvariance trade-off. In ICCV, pages 1–8, 2007. 4
  18. G. Warren, E. Schertler, and P. Bull. Detecting deception from emotional and unemotional cues. J. Nonverbal Behavior, 33(1):59–69, 2009. 2, 5
  19. S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin. Graph embedding and extensions: A general framework for dimensionality reduction.
  20. PAMI, 29(1):40–51, 2007. 5
  21. Z. Zeng, M. Pantic, G. Roisman, and T. Huang. A survey of affect recognition methods: Audio, visual, and spontaneous expressions. PAMI, 31(1):39–58, 2008. 2
  22. G. Zhao and M. Pietik¨ainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. PAMI, 29(6):915– 928, 2007. 2, 4
  23. Z. Zhou, G. Zhao, and M. Pietik¨ainen. Towards a Practical Lipreading System. In CVPR, 2011. 4
  24. http://www.divaportal.org/smash/get/diva2:83077 4/FULLTEXT01.pdf
  25. http://csjournals.com/IJCSC/PDF7-1/23 [24]
  26. https://arxiv.org/ftp/arxiv/papers/1403/1403.0485.pdf
  27. Polygraph as a Lie Detectorhttp://en.wikipedia.org/wiki/Polygraph
  28. Gautam Krishna , Chavali Sai Kumar N V , Bhavaraju Tushal , Adusumilli Venu Gopal , research paper, BLEKINGE INSTITUTE OF TECHNOLOGY AUGUST,2014
  29. https://time.com/5443204/signs-lying-body-language- experts
  30. Paul Ekman’s research paper on detecting micro expressions
  31. https://www.cbc.ca/natureofthings/features/the-seven- universal-emotions-we-wear-on-our-face
  32. Forensic strategic solution detecting deception facial expression
  33. https://www.scienceofpeople.com/microexpressions/
  34. https://www.youtube.com/watch?v=rGhOuA3rr1k - micro expressions in 4K by Patry Wezowiski-2041.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2020-09-30

Issue

Volume 4, Issue 11, September-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ritom Tamuli, Srutibanta Samantara, Shubhodeep Sarkar, Sourav Adhikari, " Lie Detection Using Facial Micro- Expresions (September 2020)" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 11, pp.25-30, September-2020.