3D Facial Expression Recognition Based on Geometric Feature Fusion

In order to overcome the problems of missing important information and di-mensionality disaster of three-dimensional (3D) facial expression, a novel method based on geometric feature fusion is proposed. This approach initially extracts distance feature vectors and angle feature vectors from key facial regions such as the eyes, mouth, and eyebrows using direct geometric feature. Subsequently, the K-nearest neighbor (K-NN) algorithm is used to obtain the distance feature vector candidate sets and angle feature vector candidate sets separately. Finally, the maximum-minimum rule is utilized to fuse these candidate sets into a single feature vector, completing the recognition of 3D facial expression. Experimental results on the BU-3DFE database demonstrate that this method achieves on overall recognition rate of 97.6%, exhibiting excellent robustness to varia-tions in facial expressions. Furthermore, this approach can serve as a valuable reference for future research in the field of 3D face recognition.