Feasibility Research on the Application of Facial Expression Recognition in College Students' Mental Health Interview

Jun Mao
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Abstract

: Using computer expression recognition technology to learn the different reflections of mental sub-health status, and then explore the possibility of using facial expression recognition method in new interviews to identify mental sub-health status through this method. Methods the sub-health self-assessment scale and symptom checklist 90 (SCL-90) were used for questionnaire survey. Twenty-one subjects were selected and divided into experimental group 1, experimental group 2 and control group through pairing. An experimental study of facial expression feedback was carried out. Results compared with the control group, the somatization, interpersonal sensitivity, anxiety and psychosis of the experimental group 1 were significantly lower than those of the control group (t=2.25, -2.45, -2.42, -2.39; p<0.05); Compared with the control group, the interpersonal sensitivity and anxiety of the experimental group 2 were significantly lower than those of the control group (t=-2 06, -2.16, -2.23; p<0.05); There was no significant difference between experimental group 1 and experimental group 2. Through the computer analysis test of the sampled data, the conclusion shows that the use of computer facial expression recognition can identify the possibility of mental sub-health status of college students.
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面部表情识别在大学生心理健康访谈中应用的可行性研究
:利用计算机表情识别技术了解心理亚健康状态的不同反映,进而探索在新的访谈中使用面部表情识别方法通过该方法识别心理亚健康状态的可能性。方法采用亚健康自评量表和症状自评量表(SCL-90)进行问卷调查。选取21例受试者,采用配对法分为实验组1、实验组2和对照组。对面部表情反馈进行了实验研究。结果与对照组比较,实验1组患者躯体化、人际敏感、焦虑、精神病均显著低于对照组(t=2.25, -2.45, -2.42, -2.39;p < 0.05);与对照组比较,实验2组的人际敏感和焦虑显著低于对照组(t=-2 06, -2.16, -2.23;p < 0.05);试验1组与试验2组间差异无统计学意义。通过对采样数据的计算机分析测试,得出结论:利用计算机面部表情识别可以识别大学生心理亚健康状态的可能性。
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