Micro-Attention for Micro-Expression recognition

Chongyang Wang, M Peng, Tao Bi, T Chen
in Journal article

Abstract

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.