Micro-expression recognition method based on progressive attention

Micro-expression is unconscious expression changes that reflect people's underlying emotions and inner states. When micro-expressions occur, their low intensity and the small facial range result in insufficient feature extraction and inaccurate localization of effective features during the recognition process, which affects recognition accuracy. To address this issue, a progressive attention multi-scale convolutional network was constructed. The network integrates a multi-scale convolutional module and a progressive attention module. First, the multi-scale convolutional module is used to learn fine-grained features from different receptive fields, extracting rich details. Then, the progressive attention module is designed to accurately locate facial motion areas and robustly extract motion features from micro-expression images through information sharing and enhancement across multiple attention maps. The proposed network was tested on SMIC, CASMEII and SAMM datasets, achieving accuracy rates of 0.826, 0.880 and 0.787, and F₁ scores of 0.817, 0.864 and 0.761, respectively. The proposed method can serve as an auxiliary tool for lie detection and early screening of mental health conditions.