ZHANG Jing, ZHANG Pei, ZHUO Li. Fuzzy Support Vector Machine Based on Color Modeling for Facial Complexion Recognition in Traditional Chinese Medicine[J]. Chinese Journal of Electronics, 2016, 25(3): 474-480. doi: 10.1049/cje.2016.05.013
Citation: ZHANG Jing, ZHANG Pei, ZHUO Li. Fuzzy Support Vector Machine Based on Color Modeling for Facial Complexion Recognition in Traditional Chinese Medicine[J]. Chinese Journal of Electronics, 2016, 25(3): 474-480. doi: 10.1049/cje.2016.05.013

Fuzzy Support Vector Machine Based on Color Modeling for Facial Complexion Recognition in Traditional Chinese Medicine

doi: 10.1049/cje.2016.05.013
Funds:  This work is supported by the National Natural Science Foundation of China (No.61370189, No.61372149, No.61471013), the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (No.CIT&TCD201304036, No.CIT&TCD20150311, No.CIT&TCD201404043), the Science and Technology Development Program of Beijing Education Committee (No.KM201410005002), the Program for New Century Excellent Talents in University (No.NCET-11- 0892)the Specialized Research Fund for the Doctoral Program of Higher Education (No.20121103110017), the Natural Science Foundation of Beijing (No.4142009) and Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality.
  • Received Date: 2014-10-10
  • Rev Recd Date: 2015-05-08
  • Publish Date: 2016-05-10
  • Face diagnosis is one of the four diagnostic methods in Traditional chinese medicine (TCM). The morbidity of the organs can be revealed from the facial complexion. Due to the ambiguity of face diagnosis in TCM, Fuzzy support vector machine (FSVM) is utilized to remove the influence of outliers. The facial complexion recognition in TCM based on FSVM is proposed in this paper, which includes the following steps: 1) The facial cheek region are segmented as skin blocks; 2) The color feature in Lab color space is extracted from skin block to represent facial complexion characteristic; 3) The fuzzy membership is calculated to obtain the membership of the training samples, in which the final membership of training samples is determined by combining membership calculation based on distance with the facial complexion recognition based on color modeling; 4) The FSVM is built to classify facial complexion characteristic. Experimental results show that proposed facial complexion recognition has better antiinterference performance as well as a higher recognition rate up to 82%.
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