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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.
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]
Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Deception detection based on micro-expression and feature selection methods.
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  Data: <searchLink fieldCode="AR" term="%22Yuan%2C+Shusen%22">Yuan, Shusen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shao%2C+Zilong%22">Shao, Zilong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Zhongjun%22">Ma, Zhongjun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Ting%22">Cao, Ting</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xing%2C+Hongbo%22">Xing, Hongbo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Yong%22">Liu, Yong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Yewen%22">Cao, Yewen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ycao@sdu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Image+%26+Video+Processing%22">EURASIP Journal on Image & Video Processing</searchLink>. 5/20/2025, Vol. 2025 Issue 1, p1-18. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Facial+expression%22">Facial expression</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Eye+tracking%22">Eye tracking</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1186/s13640-025-00674-3
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      – SubjectFull: Facial expression
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      – SubjectFull: Feature selection
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      – SubjectFull: Principal components analysis
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              Text: 5/20/2025
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