Objective: This research was conducted to explore the possible correlation between Traditional Chinese Medicine (TCM) constitutions and facial features, and improve the accuracy of constitution classification.
Methods: Automatic face detection and key point positioning were adopted to automatically detect the human face area in collected images, which were further cut into a standard size. We extracted the color and texture features of the facial image using the linear discriminant analysis classifier and studied its contribution to the classification of a constitution. The color features included RGB color space and Lab space, and the texture features included the Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG).
Results: (1) A higher classification was achieved using the Lab color space than RGB. (2) LBP, HOG and other texture features were typically better than the color features (RGB and Lab) for achieving a higher classification. LBP. (3) Although the near-infrared image was more robust for illumination changes, it lost important color information. (4) The classification accuracy of the BC was relatively high, followed by the DHC, PDC, SBC and CC. Because of the low number of image samples, the classification accuracy of the YADC, YIDC, SQC, and QDC was typically low.
Conclusion: Facial color and texture features can assist in the classification of TCM constitutions.