Micro-Expression Recognition Algorithm Using Regions of Interest and the Weighted ArcFace Loss

Micro-expressions often reveal more genuine emotions but are challenging to recognize due to their brief duration and subtle amplitudes. To address these challenges, this paper introduces a micro-expression recognition method leveraging regions of interest (ROIs). Firstly, four specific ROIs are selected based on an analysis of the optical flow and relevant action units activated during micro-expressions. Secondly, effective feature extraction is achieved using the optical flow method. Thirdly, a block partition module is integrated into a convolutional neural network to reduce computational complexity, thereby enhancing model accuracy and generalization. The proposed model achieves notable performance, with accuracies of 93.96%, 86.15%, and 81.17% for three-class recognition on the CASME II, SAMM, and SMIC datasets, respectively. For five-class recognition, the model achieves accuracies of 81.63% on the CASME II dataset and 84.31% on the SMIC dataset. Experimental results validate the effectiveness of using ROIs in improving micro-expression recognition accuracy.