International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

Lie Detection Technique using Video from the Ratio of Change in the Appearance

  • Received : 2022.07.05
  • Published : 2022.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

Lying is nuisance to all, and all liars knows it is nuisance but still keep on lying. Sometime people are in confusion how to escape from or how to detect the liar when they lie. In this research we are aiming to establish a dynamic platform to identify liar by using video analysis especially by calculating the ratio of changes in their appearance when they lie. The platform will be developed using a machine learning algorithm along with the dynamic classifier to classify the liar. For the experimental analysis the dataset to be processed in two dimensions (people lying and people tell truth). Both parameter of facial appearance will be stored for future identification. Similarly, there will be standard parameter to be built for true speaker and liar. We hope this standard parameter will be able to diagnosed a liar without a pre-captured data.

Keywords

References

  1. Learning Mind, The 6 Subtle Facial Expressions which Reveals Lie and Inauthenticity, copyright © Learning Mind 2012-2019, All Rights Reserved, https://www.learning-mind.com/facial-expressions-lies/, access date: 24th December 2019
  2. Lloyd, E. P., Deska, J. C., Hugenberg, K., McConnell, A. R., Humphrey, B., & Kunstman, J. W. (2017). Miami University deception detection video database, https://sc.lib.miamioh.edu/handle/2374.MIA/6067
  3. Shloka D, Maxwell S, Zachary M, Neural Lie Detection with the CSC Deceptive Speech Dataset, Stanford University, USA, access date: 30th January 2021.
  4. Veronica P ' erez-Rosas, Mohamed Abouelenien, Rada Mihalcea, ' Yao Xiao, CJ Linton, Mihai Burzo, erbal and Nonverbal Clues for Real-life Deception Detection, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2336-2346, Lisbon, Portugal, 17-21 September 2015. c 2015 Association for Computational Linguistics.
  5. Zhe W, Bharat S, Larry S. D, Subrahmanian V. S, Deception Detection in Videos, The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), Copyright c 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
  6. Deborah B, Jane G-D, Kevin R. B, The Impact of Presentation Modality on Perceptions of Truthful and Deceptive Confessions, Journal of Criminology, Hindawi, Volume 2013 |Article ID 164546 | https://doi.org/10.1155/2013/164546
  7. Gangeshwar K , Navonil M, Soujanya , Erik C, A Deep Learning Approach for Multimodal Deception Detection, A*STAR Artificial Intelligence Initiative (A*AI), Institute of High Performance Computing, Temasek Laboratories, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Centro de Investigacin en Computacin, IPN, Mexico, March 2018
  8. Veronica P, Rada M, Alexis N, Mihai B, A Multimodal Dataset for Deception Detection, Computer Science and Engineering, University of North Texas, Computer Science and Electrical Engineering, University of Michigan, Mechanical Engineering, University of North Texas, Computer Science, Engineering, and Physics, University of Michigan, access date: January 2021
  9. Como citar este articulo: S. Bedoya-Echeverry, H. Belalcazar-Ramirez, H. Loaiza-Correa, S. E. Nope-Rodriguez, C. R. Pinedo-Jaramillo, and A. D. Restrepo-Giron, "Detection of lies by facial thermal imagery analysis," Rev. Fac. Ing., vol. 26 (44), pp. 47-59, Ene. 2017
  10. V. Gupta, M. Agarwal, M. Arora, T. Chakraborty, R. Singh and M. Vatsa, "Bag-of-Lies: A Multimodal Dataset for Deception Detection," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 83-90, doi: 10.1109/CVPRW.2019.00016.
  11. Martinez, A. M.; Kak, A. C. (2001). "PCA versus LDA". IEEE Transactions on Pattern Analysis and Machine Intelligence. 23 (=2): 228-233. doi:10.1109/34.908974.
  12. Jiang, L., Cai, Z., Wang, D. et al. Bayesian Citation-KNN with distance weighting. Int. J. Mach. Learn. & Cyber. 5, 193-199 (2014). https://doi.org/10.1007/s13042-013-0152-x
  13. Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed.). Switzerland: Springer International Publishing.
  14. Almeida, L.B. Multilayer perceptrons, in Handbook of Neural Computation, IOP Publishing Ltd and Oxford University Press, 1997.
  15. Shubham P, The Euclidean Distance Formula, k Nearest Neighbor Classifier ( kNN )-Machine Learning Algorithms (March 2018), https://equipintelligence.medium.com/k-nearest-neighbor-classifier-knn-machine-learning-algorithms-ed62feb86582.
  16. Onel H, The KNN Algorithm, Machine Learning Basics with the K-Nearest Neighbors Algorithm (September 2018), https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm.
  17. Michael T. Brannick, The Logistic Curve, Logistic Regression, Course Materials and Research Website, http://faculty.cas.usf.edu/mbrannick/regression/Logistic.html.
  18. Amal N, A Beginner's Guide To Scikit-Learn's MLPClassifier (June 2019), COPYRIGHT ANALYTICS INDIA MAGAZINE PVT LTD, https://analyticsindiamag.com/a-beginners-guide-to-scikit-learns-mlpclassifier/.
  19. Vasco F, Equation of a Multi-Layer Perceptron Network, Data Science (Oct 2020), https://datascience.stackexchange.com/questions/84016/equation-of-a-multi-layer-perceptron-network.