Temporal-Polar Dynamics: Elevating Event-Based Micro-Expression Recognition

Micro-expressions refer to brief, subtle facial movements often concealing genuine human emotions. However, the advancement of Micro-Expression Recognition (MER) is hindered by the low frame rates of frame-based cameras. Although the successor event camera has a high frame rate, there are currently difficulties in obtaining and unstable performance issues. Drawing inspiration from the operational principles of event camera, we introduces two event features. Beyond spatial information, these features encode temporal and polarity information of event. Following local normalization, we employ the temporal polar pixel-wise interaction module to extract local feature. Additionally, we construct a temporal polar dynamic network, merging local feature with dense global optical flow to map deeper features. Experimental results demonstrate the superiority of the proposed method across multiple datasets compared to state-of-the-art approaches. This work enriches the encoding of event features, enhancing their performance in micro-expression recognition tasks and contributing to the future proliferation of event camera technology.