GLEFFN: A Global-Local Event Feature Fusion Network for Micro-Expression Recognition

Abstract

Micro-expressions are facial movements of short duration and low amplitude, which, upon analysis, can reveal genuine human emotions. However, the low frame rate of frame-based cameras hinders the further advancement of micro-expression recognition (MER). A novel technology, event-based cameras, boasting high frame rates and low latency, proves suitable for the MER task but remains challenging to obtain. In this article, a local event feature, namely the local count image, is proposed. This feature is calculated from up-sampled video using the SloMo method. Additionally, a global-local event feature fusion network is constructed, wherein the local count image and the global dense optical flow are merged to map deeper features and effectively address the MER task. Experimental results demonstrate that the proposed light-weighted method outperforms state-of-the-art approaches across multiple datasets. To our best knowledges that this work marks the first successful attempt to solve the MER task from an event perspective, thus facilitating the future promotion of event-based camera technology and providing inspiration for future research endeavors in related domains.