Artificial Intelligence based Approach to Analyze the Lie Detection using Skin and Facial Expression Artificial Intelligence using Skin and Facial Expression
Article Sidebar
Main Article Content
Abstract
Finding a secret fact that is known to one person but kept from others is the aim of lie detection. When it comes to security and legal concerns, it is crucial to be able to analyze human deceptive behavior. Based on the idea that lying causes specific emotions, which in turn cause certain physiological reactions depending on a person's level of stress, psychological lie detection is based on this premise. Modern lie detectors measure things like skin conductance, respiration, and blood pressure. The intended study presents a lie detection system that uses fuzzy analysis to integrate many factors, including skin conductance, facial expressions (eye blinking), and voice frequency, which is actually influenced by a person's degree of stress. Fuzzy is an AI technique based on graphical representations and mathematical calculations of an output which is affected by input parameters. In comparison to employing a single indicator alone, the integration of skin analysis, voice level, and facial expression using fuzzy analysis may improve the accuracy of lie detection. This analysis provides a way to predict that a person is lying when all three parameters i.e., Skin conductance, voice frequency, and blinking of eye increases which effect on stress level of a body.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Pakistan Journal Emerging Science and Technologies (PJEST) in collaboration with Govt. Islamia Graduate College Civil Lines Lahore, Pakistan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
A. Vrij and B. Verschuere, Lie detection in a forensic context. Oxford University Press, 2013.
S. Yildirim, M. S. Chimeumanu, and Z. A. Rana, "The influence of micro-expressions on deception detection," Multimedia Tools and Applications, pp. 1-19, 2023.
"Body Language Lie Detection." Lie Detection Test.uk. (accessed.
J. A. Siegel and P. J. Saukko, Encyclopedia of forensic sciences. Academic Press, 2012.
A. Bablani, D. R. Edla, V. Kupilli, and R. Dharavath, "Lie detection using fuzzy ensemble approach with novel defuzzification method for classification of EEG signals," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-13, 2021.
P. B. Rajan, "Polygraph Tests-Benefits and Challenges," Academicus International Scientific Journal, vol. 10, no. 19, pp. 146-155, 2019.
W. Chai, "What Is Fuzzy Logic?," Retrieved from TechTarget: https://www. techtarget. com/searchenterpriseai/definition/fuzzy-logic, 2021.
A. Vrij, S. A. Mann, R. P. Fisher, S. Leal, R. Milne, and R. Bull, "Increasing cognitive load to facilitate lie detection: The benefit of recalling an event in reverse order," Law and human behavior, vol. 32, pp. 253-265, 2008.
J. G. Proudfoot, J. L. Jenkins, J. K. Burgoon, and J. F. Nunamaker Jr, "More than meets the eye: How oculometric behaviors evolve over the course of automated deception detection interactions," Journal of Management Information Systems, vol. 33, no. 2, pp. 332-360, 2016.
K. Kim, J. Kim, and J.-H. Lee, "Guilt, lying, and attentional avoidance of concealed information," Social Behavior and Personality: an international journal, vol. 44, no. 9, pp. 1467-1475, 2016.
A. Vrij, R. Fisher, S. Mann, and S. Leal, "A cognitive load approach to lie detection," Journal of Investigative Psychology and Offender Profiling, vol. 5, no. 1‐2, pp. 39-43, 2008.
R. H. Nugroho, M. Nasrun, and C. Setianingsih, "Lie detector with pupil dilation and eye blinks using hough transform and frame difference method with fuzzy logic," in 2017 International conference on control, electronics, renewable energy and communications (ICCREC), 2017: IEEE, pp. 40-45.
T. A. Wibowo, M. Nasrun, and C. Setianingsih, "Lie Detector With Analysis Pupil Dilation And Eye Blinks Analysis Using Hough Transform And Decision Tree," in 2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), 2018: IEEE, pp. 172-178.
J. C. Kircher, T. Packard, B. G. Bell, P. C. Bernhard, and U. U. S. L. CITY, "Comparison of Skin Conductance and Skin Resistance Measures for the Detection of Deception," Comparison of Skin Conductance and Skin Resistance Measures for the Detection of Deception, 2001.
J. Tomash and P. Reed, "Using conditioning to elicit skin conductance responses to deception," Learning and Motivation, vol. 49, pp. 31-37, 2015.
Y.-F. Lai, M.-Y. Chen, and H.-S. Chiang, "Constructing the lie detection system with fuzzy reasoning approach," Granular Computing, vol. 3, pp. 169-176, 2018.
A. Chakraborty, A. Konar, U. K. Chakraborty, and A. Chatterjee, "Emotion recognition from facial expressions and its control using fuzzy logic," IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 39, no. 4, pp. 726-743, 2009.
A. Królak and P. Strumiłło, "Eye-blink detection system for human–computer interaction," Universal Access in the Information Society, vol. 11, pp. 409-419, 2012.
X. Shen, G. Fan, C. Niu, and Z. Chen, "Catching a liar through facial expression of fear," Frontiers in Psychology, vol. 12, p. 675097, 2021.
M. Monaro, S. Maldera, C. Scarpazza, G. Sartori, and N. Navarin, "Detecting deception through facial expressions in a dataset of videotaped interviews: A comparison between human judges and machine learning models," Computers in Human Behavior, vol. 127, p. 107063, 2022.
N. Rodriguez-Diaz, D. Aspandi, F. Sukno, and X. Binefa, "Machine Learning-Based Lie Detector applied to a Novel Annotated Game Dataset. Future Internet 2022, 14, 2," ed: s Note: MDPI stays neutral with regard to jurisdictional claims in published …, 2021.
C. S. Barathi, "Lie detection based on facial micro expression body language and speech analysis," International Journal of Engineering Research & Technology, vol. 5, no. 2, pp. 337-343, 2016.
K. X. Beh and K. M. Goh, "Micro-expression spotting using facial landmarks," in 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA), 2019: IEEE, pp. 192-197.
G. Krishnamurthy, N. Majumder, S. Poria, and E. Cambria, "A deep learning approach for multimodal deception detection," in International Conference on Computational Linguistics and Intelligent Text Processing, 2018: Springer, pp. 87-96.
C. K. R. Hoff, "THE SPECIFIC APPLICATION OF POLYGRAPH FOR INSURANCE COMPANIES," Polygraph, vol. 11, no. 2, pp. 152-158, 1982.
M. G. Frank and T. H. Feeley, "To catch a liar: Challenges for research in lie detection training," Journal of Applied Communication Research, vol. 31, no. 1, pp. 58-75, 2003.