Convolutional Attention Based Mechanism for Facial Microexpression Recognition

Unanticipated and rapid change in facial expression are micro-expression (ME) that are hard to hide after an emotionally charged event. Facial microexpressions are transient and subtle, making identification challenging. Recognition of MEs are very crucial in the light of personal intention phase identification. Previous studies had challenges recognizing ME due to complicated spatiotemporal linkage in video data. Using the ConvMixer architecture, we Proposed a novel technique for facial microexpression identification based on convolutional attention mechanism. The research uses SAMM, SMIC, and CASME-II are benchmark datasets used to perform experiments. ConvMixer deployed to analyze the SAMM dataset where ConvMixer achieved an amazing 99.73% accuracy, 97.3% precision, 96.5% recall, and 99% F1-Score while 10-fold cross-validation. In addition, we extended our analysis to the CASME-II dataset, where ConvMixer attained an F1-Score of 99.4%, an accuracy of 99.12%, a precision of 98.3%, and a recall of 98.7%. These findings indicate that ConvMixer regularly outperforms other MER architectures, while capturing video specific and dynamic characteristics. ConvMixer architecture are good in capturing both spatial and temporal correlations and extracts spatial information using depthwise convolutions and channel mixing processes. High F1-Score, recall, precision, and accuracy across several datasets demonstrate the robustness and adaptability of the ConvMixer architecture. Finally, our findings show that the Convolutional Attention-Based Mechanism for facial microexpression recognition (CABM-FMER) works effectively for identifying facial MEs.