基于面部图像特征融合的中医望诊面色分类研究  

Facial color classification of traditional Chinese medicine inspection based on fusion of facial image features

作　　者：林怡 王斌[1] 许家佗[2] 屠立平[2] LIN Yi;WANG Bin;XU Jiatuo;TU Liping(College of Information Engineering,Nanjing University of Finance and Economics,Nanjing,Jiangsu,210023;College of Basic Medicine,Shanghai University of Traditional Chinese Medicine,Shanghai,201203)

机构地区：[1]南京财经大学信息工程学院,江苏南京210046 [2]上海中医药大学基础医学院,上海201203

出　　处：《实用临床医药杂志》2020年第14期1-5,共5页Journal of Clinical Medicine in Practice

基　　金：国家自然科学基金资助项目(61372158);国家自然科学基金项目(81873235);国家重点研发计划中医药现代化研究重点专项项目(2017YFC1703301);江苏省自然科学基金(BK20181414);江苏省高校优秀科技创新团队项目(2017-15);江苏省研究生科研与实践创新计划项目(KYCX18_1441);2017年度军队后勤科研重点项目(BWS17J028)

摘　　要：根据中医相关理论,面色分为赤、黄、白、黑4大类,利用深度学习方法可实现面部图像的关键点识别和感兴趣区域的自动分割。本研究创新性地结合颜色空间特征、面部纹理统计特征、唇部颜色特征等要素,使用多种机器学习方法对提取到的面部特征进行分类识别。为验证所提出方法的有效性,使用专业仪器采集了575幅人脸图像组成数据库,并在中医专家指导下进行面色标定。本研究结果显示,融合面部皮肤颜色特征、面部纹理特征、唇部颜色特征的最佳识别率可达91.03%,颜色特征是中医面色分类识别最重要的特征之一。According to the theory of traditional Chinese medicine,the facial complexions are divided into four categories named as red,yellow,white and black,and deep learning method is used to realize the key points recognition and automatic segmentation of interested region.This study innovatively combines elements such as color space features,facial texture statistical features,and lip color features,and uses a variety of machine learning methods to classify and recognize the extracted facial features.In order to verify the effectiveness of the proposed method,575 facial images are collected by professional instruments to form a database,and the face color is calibrated under the guidance of experts of traditional Chinese medicine.The result showed that the best recognition rate of the fusion of facial skin color features,texture features and lip color features reached 91.03%,Color feature is one of the most important features of classification and recognition.

关 键 词：图像处理 中医望诊 面色分类 特征融合 计算机视觉 纹理特征 唇色特征 人脸识别 

分 类 号：R241[医药卫生—中医诊断学]