Micro-Attention for Micro-Expression recognition

Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into micro-expression recognition areas. Whilst the higher recognition accuracy achieved with deep learning methods, substantial challenges in micro-expression recognition remain. Issues with the existence of micro expression in small-local areas on face and limited size of databases still constrain the recognition accuracy of such facial behavior. In this work, to tackle such challenges, we propose novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial area of interest (action units). Moreover, coping with small datasets, a simple yet efficient transfer learning approach is utilized to alleviate the overfitting risk. With an extensive experimental evaluation on two benchmarks (CASMEII, SAMM) and post-hoc feature visualizations, we demonstrate the effectiveness of proposed micro-attention and push the boundary of automatic recognition of micro-expression.