Enhancing Micro-Expression Recognition with Broadbent Attention Mechanism: A High-Performance Approach for Emotion Detection

Micro-expression recognition (MER) is a critical task in affective computing, yet it remains challenging due to the subtle, brief, and involuntary nature of micro-expressions. Existing methods often find it difficult to spot these quick micro facial expressions (MFEs), especially when there are obstacles, background noise, or differences between people. In this study, we propose a novel approach that integrates the Broadbent Attention Mechanism into a Convolutional Neural Network (CNN) to enhance MER. This attention mechanism is designed to focus on the most informative regions of facial images, improving the model's ability to extract relevant features and discard irrelevant information. The model is evaluated on the SAMM dataset, covering various emotions. Extensive experiments are conducted using both K-fold and Stratified K-fold cross-validation techniques to ensure a robust evaluation process. The proposed method achieves high accuracy 99.72%, precision 99.73%, Recall 99.72% and f1-Score 99.72% with Stratified K-fold & accuracy 99.65%, precision 99.65%, Recall 99.66% and f'1-Score 99.65% with K-fold, outperforming baseline models and demonstrating its effectiveness in recognizing subtle micro-expressions. These results indicate that the Broadbent Attention Mechanism significantly enhances accuracy and robustness of MER systems, offering a promising solution for applications in human-computer interaction (HCI) and emotion analysis.