Citation

Gan, Wei Ci and Chung, Gwo Chin and Lee, It Ee and Chan, Kah Yoong and Tan, Soo Fun (2025) Detection of Lying Behaviors Based on Facial Micro-Expressions Using Artificial Neural Network. International Journal of Technology, 16 (4). p. 1283. ISSN 2086-9614

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Abstract

Lying is the deliberate provision of false information with the intent to deceive others by concealing the truth. Traditionally, lie detection has been conducted using a polygraph machine, which measures physiological responses to identify signs of lying. However, it might not be accurate because people can undergo training to hide these behaviors detected by the polygraph. A modern way to address this problem is by observing the facial micro-expression that is unintentionally produced by the facial muscles. In this research, the main objective is to develop a deep learning model to detect possible lying behaviours based on facial micro-expressions. A facial behavior analysis toolkit, named OpenFace 2.0, was used to extract different categories of facial muscles into Action Units (AU) from the video frames. The data obtained were trained by Artificial Neural Network (ANN) to classify AU, which has the possible lying frames. Different case studies were also made based on different hyperparameters for performance evaluation. For all case studies, the training and testing accuracies can achieve performances of approximately 80%-90%, and the prediction on unseen data has a record of 55%-70% accurate prediction. Therefore, the detection of lying behaviors based on facial micro-expressions using deep learning is possible, and the result obtained from this research is crucial for the development of a more complete and advanced lying detection system to assist authorities in fighting crimes.

Item Type:	Article
Uncontrolled Keywords:	Artificial neural network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 02:37
Last Modified:	06 Oct 2025 03:24
URII:	http://shdl.mmu.edu.my/id/eprint/14542

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