Deception detection based on micro-expression and feature selection methods
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 dete...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | EURASIP Journal on Image and Video Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13640-025-00674-3 |
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| _version_ | 1849326007903846400 |
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| author | Shusen Yuan Zilong Shao Zhongjun Ma Ting Cao Hongbo Xing Yong Liu Yewen Cao |
| author_facet | Shusen Yuan Zilong Shao Zhongjun Ma Ting Cao Hongbo Xing Yong Liu Yewen Cao |
| author_sort | Shusen Yuan |
| collection | DOAJ |
| description | 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) $$^{2}$$ 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. |
| format | Article |
| id | doaj-art-e28e6711f43b4d38bf5a8ee857a0b675 |
| institution | Kabale University |
| issn | 1687-5281 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj-art-e28e6711f43b4d38bf5a8ee857a0b6752025-08-20T03:48:15ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812025-05-012025111810.1186/s13640-025-00674-3Deception detection based on micro-expression and feature selection methodsShusen Yuan0Zilong Shao1Zhongjun Ma2Ting Cao3Hongbo Xing4Yong Liu5Yewen Cao6School of Information Science and Engineering, Shandong UniversitySchool of Information Science and Engineering, Shandong UniversityShandong Future Network Research InstituteReddit IncSchool of Information Science and Engineering, Shandong UniversitySchool of Information Science and Engineering, Shandong UniversitySchool of Information Science and Engineering, Shandong UniversityAbstract 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) $$^{2}$$ 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.https://doi.org/10.1186/s13640-025-00674-3Micro-expressionDeception detectionFisher scorePCA |
| spellingShingle | Shusen Yuan Zilong Shao Zhongjun Ma Ting Cao Hongbo Xing Yong Liu Yewen Cao Deception detection based on micro-expression and feature selection methods EURASIP Journal on Image and Video Processing Micro-expression Deception detection Fisher score PCA |
| title | Deception detection based on micro-expression and feature selection methods |
| title_full | Deception detection based on micro-expression and feature selection methods |
| title_fullStr | Deception detection based on micro-expression and feature selection methods |
| title_full_unstemmed | Deception detection based on micro-expression and feature selection methods |
| title_short | Deception detection based on micro-expression and feature selection methods |
| title_sort | deception detection based on micro expression and feature selection methods |
| topic | Micro-expression Deception detection Fisher score PCA |
| url | https://doi.org/10.1186/s13640-025-00674-3 |
| work_keys_str_mv | AT shusenyuan deceptiondetectionbasedonmicroexpressionandfeatureselectionmethods AT zilongshao deceptiondetectionbasedonmicroexpressionandfeatureselectionmethods AT zhongjunma deceptiondetectionbasedonmicroexpressionandfeatureselectionmethods AT tingcao deceptiondetectionbasedonmicroexpressionandfeatureselectionmethods AT hongboxing deceptiondetectionbasedonmicroexpressionandfeatureselectionmethods AT yongliu deceptiondetectionbasedonmicroexpressionandfeatureselectionmethods AT yewencao deceptiondetectionbasedonmicroexpressionandfeatureselectionmethods |