Identifying Psychological Symptoms Based on Facial Movements

Xiaoyang Wang; Yilin Wang; Mingjie Zhou; Baobin Li; Xiaoqian Liu; Tingshao Zhu

doi:10.3389/fpsyt.2020.607890

Identifying Psychological Symptoms Based on Facial Movements

Front Psychiatry. 2020 Dec 15:11:607890. doi: 10.3389/fpsyt.2020.607890. eCollection 2020.

Authors

Xiaoyang Wang^{1

2}, Yilin Wang^{1

2}, Mingjie Zhou^{1

2}, Baobin Li^{2

3}, Xiaoqian Liu^{1

2}, Tingshao Zhu^{1

2}

Affiliations

¹ Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
² Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
³ School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China.

Abstract

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.

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