通过解码面部动作单元序列揭开心理健康的神秘面纱

Deepika Sharma, Jaiteg Singh, Sukhjit Singh Sehra, S. Sehra
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引用次数: 0

摘要

心理健康对于有效的日常运作和压力管理是不可或缺的。面部表情可以提供有关一个人精神状态的重要线索,因为它们在不同文化中具有普遍一致性。本研究旨在利用面部动作单元(AU)通过面部微表情检测情绪差异,从而识别可能存在的心理健康问题。此外,还使用卷积神经网络(CNN)对微表情进行检测和分类。此外,还使用 K-means 方程对 AUs 组合进行了识别,以划分微表情类别。模型的训练和评估采用了两个基准数据集 CASME II 和 SAMM。模型在 CASME II 和 SAMM 数据集上的准确率分别达到了 95.62% 和 93.21%。随后,利用所提出的框架进行了病例分析,以识别抑郁症患者,准确率达到 92.99%。该实验揭示了一个事实,即厌恶、悲伤、愤怒和惊讶等情绪是抑郁症患者在交流过程中体验到的主要情绪。研究结果表明,利用面部动作单元进行微表情检测为心理健康诊断提供了一种前景广阔的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demystifying Mental Health by Decoding Facial Action Unit Sequences
Mental health is indispensable for effective daily functioning and stress management. Facial expressions may provide vital clues about the mental state of a person as they are universally consistent across cultures. This study intends to detect the emotional variances through facial micro-expressions using facial action units (AUs) to identify probable mental health issues. In addition, convolutional neural networks (CNN) were used to detect and classify the micro-expressions. Further, combinations of AUs were identified for the segmentation of micro-expressions classes using K-means square. Two benchmarked datasets CASME II and SAMM were employed for the training and evaluation of the model. The model achieved an accuracy of 95.62% on CASME II and 93.21% on the SAMM dataset, respectively. Subsequently, a case analysis was done to identify depressive patients using the proposed framework and it attained an accuracy of 92.99%. This experiment revealed the fact that emotions like disgust, sadness, anger, and surprise are the prominent emotions experienced by depressive patients during communication. The findings suggest that leveraging facial action units for micro-expression detection offers a promising approach to mental health diagnostics.
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