Analysis and Recognition of Voluntary Facial Expression Mimicry Based on Depressed Patients

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 8; pp. 3698 - 3709
• Main Authors: Ye, Jiayu, Yu, Yanhong, Fu, Gang, Zheng, Yunshao, Liu, Yang, Zhu, Yitao, Wang, Qingxiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Algorithms; Anger; Attention; Bioinformatics; Cognitive ability; Correlation analysis; Correlation of Data; Deep learning; Depression; Disgust; E-ResNet; E-ViT; Emotions; Face recognition; Facial Expression; Facial features; Fear; Feature extraction; Feature selection; FER; Happiness; Humans; Imitative Behavior; Logistic Models; Middle Aged; Mimicry; Pattern recognition; Random Forest; Sadness; Support vector machines; VFEM; Young Adult

Many clinical studies have shown that facial expression recognition and cognitive function are impaired in depressed patients. Different from spontaneous facial expression mimicry (SFEM), 164 subjects (82 in a case group and 82 in a control group) participated in our voluntary facial expression mimi...