Deception detection based on micro-expression and feature selection methods.
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| Title: | Deception detection based on micro-expression and feature selection methods. |
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| Authors: | Yuan, Shusen1 (AUTHOR), Shao, Zilong1 (AUTHOR), Ma, Zhongjun2 (AUTHOR), Cao, Ting3 (AUTHOR), Xing, Hongbo1 (AUTHOR), Liu, Yong1 (AUTHOR), Cao, Yewen1 (AUTHOR) ycao@sdu.edu.cn |
| Source: | EURASIP Journal on Image & Video Processing. 5/20/2025, Vol. 2025 Issue 1, p1-18. 18p. |
| Subjects: | Facial expression, Feature selection, Principal components analysis, Support vector machines, Artificial intelligence, Eye tracking |
| Abstract: | Video-based deception detection, which identifies lies through facial expressions and behaviors, has proven to be an effective approach in criminal interrogation. In this paper, a deception detection framework is proposed that incorporates a novel set of features and a unique deception detection method based on facial expressions, particularly micro-expressions. Two feature selection methods are applied to optimize these features. Specifically, facial action units (AUs), eye gaze, and head pose were extracted using the OpenFace toolkit, while micro-expression information was obtained via the SOFTNet model, trained on the CAS(ME) 2 data set. A sequential combination of the Fischer Score and Principal Component Analysis (PCA) was employed for feature selection, with a Support Vector Machine (SVM) used for classification. Feature importance analysis indicated that micro-expression (ME) information had a significant impact on the deception detection task. The proposed framework was evaluated on two publicly available data sets, achieving accuracies of 98.07% and 98.23% on the real-life and MU3D data sets, respectively, thus demonstrating its superiority over prior approaches in the literature. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Video-based deception detection, which identifies lies through facial expressions and behaviors, has proven to be an effective approach in criminal interrogation. In this paper, a deception detection framework is proposed that incorporates a novel set of features and a unique deception detection method based on facial expressions, particularly micro-expressions. Two feature selection methods are applied to optimize these features. Specifically, facial action units (AUs), eye gaze, and head pose were extracted using the OpenFace toolkit, while micro-expression information was obtained via the SOFTNet model, trained on the CAS(ME) 2 data set. A sequential combination of the Fischer Score and Principal Component Analysis (PCA) was employed for feature selection, with a Support Vector Machine (SVM) used for classification. Feature importance analysis indicated that micro-expression (ME) information had a significant impact on the deception detection task. The proposed framework was evaluated on two publicly available data sets, achieving accuracies of 98.07% and 98.23% on the real-life and MU3D data sets, respectively, thus demonstrating its superiority over prior approaches in the literature. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 16875176 |
| DOI: | 10.1186/s13640-025-00674-3 |
