Region Symmetry Mask for TCM-based Face Shape Classification

In TCM (Traditional Chinese Medicine) facial diagnosis, facial morphology features are essential for symptom differentiation and treatment selection. Face shape, as one of the facial morphology features, can help TCM physicians understand the patient’s condition by observing the patient's overall appearance. Existing face shape classification methods do not use the face shape dataset based on the TCM theory and do not consider the symmetry of the face shape, which leads to the poor generalization performance of the model. This paper establishes a new TCM-based face shape dataset annotated by several TCM physicians and proposes a new mechanism RSM (Region Symmetry Mask) for face shape classification. The key points of the face are detected by face detection technology, which is then processed to get the region symmetry mask map of the cheeks. The region symmetry mask map can guide the model to extract strongly correlated features. Finally, the classifier is used to output the categories of the face shape. The experimental results on the new TCM-based face shape dataset show that the accuracy of the method with RSM is increased by 3%-5% compared with other methods, indicating that the RSM mechanism is effective for face shape classification.