| Autor: |
Shuai Gao, Lin Chen, Yuancheng Fang, Shengbing Xiao, Hui Li, Xuezhi Yang, Rencheng Song |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Open Journal of the Computer Society, Vol 5, Pp 660-670 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2644-1268 |
| DOI: |
10.1109/OJCS.2024.3485688 |
| Popis: |
Deception detection is essential for protecting the public interest and maintaining social order. Its application in various fields helps to establish a safer and trustworthy social environment. This study focuses on the problem of deception detection in videos and proposes a visual deception detection method based on a capsule network (DDCapsNet). The DDCapsNet model predicts deception classification using the fusion of facial expression features and video-based heart rate feature via a channel attention mechanism. Supervised contrastive learning is further introduced to enhance the generalization ability of the DDCapsNet. The proposed model is evaluated on a self-collected dataset (physiological-assisted visual deception detection dataset, PV3D) and the public Bag-of-Lies (BOL) dataset, respectively. The results show that DDCapsNet outperforms the unimodal system and other state-of-the-art (SOTA) methods, where the ACC reaches 77.97% and the AUC reaches 78.45% on PV3D, and the ACC reaches 73.19% and the AUC reaches 72.78% on BOL dataset. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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