{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:14:30Z","timestamp":1791504870895,"version":"4.3.4"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:00:00Z","timestamp":1607990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> 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.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> 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.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The correlation coefficients between the predicted values and actual scores were 0.26 and 0.42 (<jats:italic>P<\/jats:italic> &amp;lt; 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)] (<jats:italic>P<\/jats:italic> &amp;lt; 0.001).<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> 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.<\/jats:p>","DOI":"10.3389\/fpsyt.2020.607890","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T17:47:49Z","timestamp":1610300869000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Identifying Psychological Symptoms Based on Facial 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