Facial complexion recognition based on supervised latent Dirichlet allocation in TCM

The recognition of facial complexion plays an important role in facial diagnosis, which is an important part of inspection in Traditional Chinese Medicine (TCM). In this paper, we proposed intelligent techniques for recognition of facial complexion, which is classified into six classes, including normal, cyan, red, yellow, black, and white, based on the traditional theories of TCM. Quantification color histogram was employed to extract features from skin blocks of facial complexion images in RGB color space. These features were then converted to one-dimensional features, after that these were used to construct a classification model for recognition of facial complexion by supervised latent Dirichlet allocation (sLDA), which provides useful descriptive statistics for a collection, which facilitates tasks like browsing, searching, assessing document similarity, and classifying. We made experiments versus LDA feature based SVM. The experimental results showed that our method exhibited good average accuracy.