Thumbnail
Access Restriction
Open

Author Huan, Er-Yang ♦ Wen, Gui-Hua ♦ Zhang, Shi-Jun ♦ Li, Dan-Yang ♦ Hu, Yang ♦ Chang, Tian-Yuan ♦ Wang, Qing ♦ Huang, Bing-Lin
Editor Imoto, Seiya
Source Hindawi
Content type Text
Publisher Hindawi
File Format PDF
Copyright Year ©2017
Language English
Abstract Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.
ISSN 1748670X
Learning Resource Type Article
Publisher Date 2017-10-18
Rights License This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e-ISSN 17486718
Journal Computational and Mathematical Methods in Medicine
Volume Number 2017
Page Count 9


Open content in new tab

   Open content in new tab