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LDNet: A Robust Hybrid Approach for Lie Detection Using Deep Learning Techniques

Shanjita Akter Prome1, Md Rafiqul Islam2,*, Md. Kowsar Hossain Sakib1, David Asirvatham1, Neethiahnanthan Ari Ragavan3, Cesar Sanin2, Edward Szczerbicki4

1 School of Computer Science, Taylor’s University, Subang Jaya, 47500, Malaysia
2Information Systems, Australian Institute of Higher Education, Sydney, NSW 2000, Australia
3 Faculty of Social Sciences & Leisure Management, Taylor’s University, Subang Jaya, 47500, Malaysia
4 Faculty of Management and Economics, Gdansk University of Technology, Gdansk, 80-233, Poland

* Corresponding Author: Md Rafiqul Islam. Email: email

Computers, Materials & Continua 2024, 81(2), 2845-2871. https://doi.org/10.32604/cmc.2024.055311

Abstract

Deception detection is regarded as a concern for everyone in their daily lives and affects social interactions. The human face is a rich source of data that offers trustworthy markers of deception. The deception or lie detection systems are non-intrusive, cost-effective, and mobile by identifying facial expressions. Over the last decade, numerous studies have been conducted on deception detection using several advanced techniques. Researchers have focused their attention on inventing more effective and efficient solutions for the detection of deception. So, it could be challenging to spot trends, practical approaches, gaps, and chances for contribution. However, there are still a lot of opportunities for innovative deception detection methods. Therefore, we used a variety of machine learning (ML) and deep learning (DL) approaches to experiment with this work. This research aims to do the following: (i) review and analyze the current lie detection (LD) systems; (ii) create a dataset; (iii) use several ML and DL techniques to identify lying; and (iv) create a hybrid model known as LDNet. By combining layers from Vgg16 and DeneseNet121, LDNet was developed and offered the best accuracy (99.50%) of all the models. Our developed hybrid model is a great addition that significantly advances the study of LD. The findings from this research endeavor are expected to advance our understanding of the effectiveness of ML and DL techniques in LD. Furthermore, it has significant practical applications in diverse domains such as security, law enforcement, border control, organizations, and investigation cases where accurate lie detection is paramount.

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APA Style
Prome, S.A., Islam, M.R., Sakib, M.K.H., Asirvatham, D., Ragavan, N.A. et al. (2024). Ldnet: A robust hybrid approach for lie detection using deep learning techniques. Computers, Materials & Continua, 81(2), 2845-2871. https://doi.org/10.32604/cmc.2024.055311
Vancouver Style
Prome SA, Islam MR, Sakib MKH, Asirvatham D, Ragavan NA, Sanin C, et al. Ldnet: A robust hybrid approach for lie detection using deep learning techniques. Comput Mater Contin. 2024;81(2):2845-2871 https://doi.org/10.32604/cmc.2024.055311
IEEE Style
S.A. Prome et al., “LDNet: A Robust Hybrid Approach for Lie Detection Using Deep Learning Techniques,” Comput. Mater. Contin., vol. 81, no. 2, pp. 2845-2871, 2024. https://doi.org/10.32604/cmc.2024.055311



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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