Facial color classification of traditional Chinese medicine inspection based on fusion of facial image features
-
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。Abstract: 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.
-
-
LU A P. Theory of traditional Chinese medicine and therapeutic method of diseases[J]. World J Gastroenterol, 2004, 10(13): 1854.
梁爽. 《内经》色诊理论研究[D]. 济南: 山东中医药大学, 2014. DRAKE L. Prevention of blindness from diabetes mellitus-report of a WHO consultation [J]. Nurs Stand, 2007, 21(32): 30-37.
KING D E. Dlib-ml: A machine learning toolkit[J]. Journal of Machine Learning Research, 2009, 10(3): 1755-1758.
HSU R L, ABDEL-MOTTALEB M, JAIN A K. Face detection in color images[J]. IEEE Trans Pattern Anal Machine Intell, 2002, 24(5): 696-706.
YANG Y, ZHANG J, ZHUO L, et al. Cheek region extraction method for face diagnosis of Traditional Chinese Medicine[C] //Signal Processing(ICSP), 2012 IEEE 11th International Conference on. IEEE, 2012: 21-25.
ZHUO L, YANG Y C, ZHANG J, et al. Human facial complexion recognition of traditional Chinese medicine based on uniform color space[J]. Int J Patt Recogn Artif Intell, 2014, 28(4): 1450008.
ZHANG J, HOU S J, WANG J, et al. Classification of traditional Chinese medicine constitution based on facial features in color images[J]. J Tradit Chin Med Sci, 2016, 3(3): 141-146.
张晓航, 石清磊, 王斌, 等. 机器学习算法在中医诊疗中的研究综述[J]. 计算机科学, 2018, 45(S2): 32-36. 陈梦竹. 基于肤色检测的中医面色识别[D]. 北京: 北京交通大学, 2018. 吴暾华. 面向中医面诊诊断信息提取的若干关键技术研究[D]. 厦门: 厦门大学, 2008. HUAN E Y, WEN G H, ZHANG S J, et al. Deep convolutional neural networks for classifying body constitution based on face image[J]. Comput Math Methods Med, 2017, 2017: 9846707.
杨云聪. 基于图像分析的中医面色识别方法研究[D]. 北京: 北京工业大学, 2013. 侯国松. 基于条件生成对抗网络的皮肤分割与面色分类[D]. 合肥: 合肥工业大学, 2019. MIYAMOTO K, TAKIWAKI H, HILLEBRAND G G, et al. Development of a digital imaging system for objective measurement of hyperpigmented spots on the face[J]. Ski Res Technol, 2002, 8(4): 227-235.
HU M C, LAN K C, FANG W C, et al. Automated tongue diagnosis on the smartphone and its applications[J]. Comput Methods Programs Biomed, 2019, 174: 51-64.
计量
- 文章访问数: 1264
- HTML全文浏览量: 206
- PDF下载量: 157