Identifying Psychological Symptoms Based on Facial Movements - Wang, Xiaoyang; Wang, Yilin; Zhou, Mingjie; Li, Baobin; Liu, Xiaoqian; Zhu, Tingshao (Frontiers in Psychiatry, 2020)
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 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 to 0.42 (P < .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 < .001). Conclusion: The validity and reliability of facial prediction models were confirmed for the measurement of mental health based on 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.