Paper Info
Title
Objective Class-Based Micro-Expression Recognition Under Partial Occlusion Via Region-Inspired Relation Reasoning Network
Abstract
Micro-expression recognition ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MER</i> ) has attracted the attention of many researchers in the past decade. However, occlusion occurs for MER in real-world scenarios. In this paper, a challenging issue in MER that is interesting but unexplored, i.e., occlusion MER, is deeply investigated. First, to research MER under real-world occlusion conditions, synthetic occluded microexpression databases are created by using various community masks. Second, to suppress the influence of occlusion, a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> egion-inspired <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> elation <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> easoning <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etwork ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RRRN</i> ) is proposed to model the relations between various facial regions. The RRRN consists of a backbone network, a region-inspired (RI) module and a relation reasoning (RR) module. More specifically, the backbone network aims to extract feature representations from different facial regions, the RI module is designed to compute the adaptive weight from the facial region itself based on the unobstructedness and importance of the region for suppressing the influence of occlusion using an attention mechanism, and the RR module exploits the progressive interactions among these regions by performing graph convolutions. Experiments are conducted on two tasks of MEGC 2018: the holdout-database evaluation task and the composite database evaluation task. Experimental results show that RRRN can be utilized to significantly explore the importance of facial regions and capture the cooperative complementary relationship of facial regions for MER. The results also demonstrate that RRRN outperforms the state-of-the-art approaches, especially with respect to occlusion, where RRRN is more robust.
Year
DOI
Venue
2022
10.1109/TAFFC.2022.3197785
IEEE Transactions on Affective Computing
Keywords
DocType
Volume
Micro-expression recognition,occlusion,relation graph,self-attention
Journal
13
Issue
ISSN
Citations 
4
1949-3045
0
PageRank 
References 
Authors
0.34
41
5
Name
Order
Citations
PageRank
Qirong Mao126134.29
Ling Zhou200.34
Wenming Zheng3124080.70
Xiuyan Shao400.34
Xiaohua Huang549128.65