中医药信息2024，Vol.41Issue(10)：59-63,76,6.DOI:10.19656/j.cnki.1002-2406.20241011
基于人脸分析技术探究惊悸不安人群的面部特征
Exploring Facial Features of Individuals with Anxiety Using Facial Analysis Technology
摘要
Abstract
Objective:To explore the facial features of individuals with anxiety using facial key point detection technology.Methods:The study utilized an active appearance model for facial key point detection to automatically identify 68 facial key points.Based on traditional Chinese medicine(TCM)facial diagnosis experience and relevant discussions in the"Handbook of Anthropometry,"the study employed an exponential description method to extract five facial morphology data points,achieving a precise digital representation of facial diagnostic information.The observation group consisted of 147 cases from the outpatient clinics of Shandong Provincial Hospital of Traditional Chinese Medicine and Qilu Hospital,who met the criteria of the"State of Anxiety Assessment Scale."The control group included 145 normal individuals from Shandong University of Traditional Chinese Medicine.SPSS 26.0 software was used to compare the five facial morphology data points between the observation and control groups,identifying statistically significant differences.Results:Normality tests of the five facial morphology data points showed that R1 and R4 data followed a normal distribution,while R2,R3,and R5 did not.Independent sample t-tests for R1 and R4 indicated statistically significant differences.Rank sum tests for R2,R3,and R5 revealed statistically significant differences for R3,but not for R2 and R5.Conclusion:There are differences in facial morphology data between individuals with anxiety and normal individuals.The active appearance model can effectively extract facial features of individuals with anxiety and normal individuals.关键词
人脸分析技术/面诊/心理紊乱状态/主动外观模型Key words
Facial analysis technology/Facial diagnosis/Psychological disorder/Active appearance model引用本文复制引用
汪素梅,程文龙,盖路路,齐向华..基于人脸分析技术探究惊悸不安人群的面部特征[J].中医药信息,2024,41(10):59-63,76,6.基金项目
山东省重点研发计划(重大科技创新工程)项目(2021LCZX06) （重大科技创新工程）
