Identifying Psychological Symptoms Based on Facial Movements

Frontiers in Psychiatry

Mingjie Zhou¹  Tingshao Zhu¹  Yilin Wang¹  Xiaoqian Liu¹  Xiaoyang Wang¹  Baobin Li³ 

[1] Department of Psychology, University of Chinese Academy of Sciences, Beijing, China;Institute of Psychology, Chinese Academy of Sciences, Beijing, China;School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China;

关键词: mental health;    psychological symptoms;    SCL-90;    facial movements;    machine learning;    multitrait-multimethod matrix;   

DOI  :  10.3389/fpsyt.2020.607890

来源: DOAJ

【 摘 要 】

Background: Many methods have been proposed to automatically identify the presence of mental illness, but these have mostly focused on one specific mental illness. In some non-professional scenarios, it would be more helpful to understand an individual's mental health status from all perspectives.Methods: We recruited 100 participants. Their multi-dimensional psychological symptoms of mental health were evaluated using the Symptom Checklist 90 (SCL-90) and their facial movements under neutral stimulation were recorded using Microsoft Kinect. We extracted the time-series characteristics of the key points as the input, and the subscale scores of the SCL-90 as the output to build facial prediction models. Finally, the convergent validity, discriminant validity, criterion validity, and the split-half reliability were respectively assessed using a multitrait-multimethod matrix and correlation coefficients.Results: The correlation coefficients between the predicted values and actual scores were 0.26 and 0.42 (P < 0.01), which indicated good criterion validity. All models except depression had high convergent validity but low discriminant validity. Results also indicated good levels of split-half reliability for each model [from 0.516 (hostility) to 0.817 (interpersonal sensitivity)] (P < 0.001).Conclusion: The validity and reliability of facial prediction models were confirmed for the measurement of mental health based on the SCL-90. Our research demonstrated that fine-grained aspects of mental health can be identified from the face, and provided a feasible evaluation method for multi-dimensional prediction models.

【 授权许可】

Unknown