# Region attention and graph embedding network for occlusion objective class-based micro-expression recognition

arXiv: https://arxiv.org/abs/2107.05904 · HTML: https://mlsanity.com/paper/2107.05904/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 68 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition](https://mlsanity.com/paper/1905.04075/) (2019)
- [OFF-ApexNet on Micro-expression Recognition System](https://mlsanity.com/paper/1805.08699/) (2018)
- [Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition](https://mlsanity.com/paper/1902.03634/) (2019)
- [Less is More: Micro-expression Recognition from Video using Apex Frame](https://mlsanity.com/paper/1606.01721/) (2016)
- [Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition](https://mlsanity.com/paper/2006.09674/) (2020)
- [Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition](https://mlsanity.com/paper/1805.08417/) (2018)
- [Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions](https://mlsanity.com/paper/1901.04656/) (2019)
- [LEARNet Dynamic Imaging Network for Micro Expression Recognition](https://mlsanity.com/paper/1904.09410/) (2019)
- [MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network](https://mlsanity.com/paper/2004.08915/) (2020)
- [Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes](https://mlsanity.com/paper/1405.1207/) (2014)
- [Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks](https://mlsanity.com/paper/1904.01390/) (2019)
- [Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition](https://mlsanity.com/paper/2005.06040/) (2020)
- [A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challenges](https://mlsanity.com/paper/1806.05781/) (2018)
- [ImageNet pre-trained models with batch normalization](https://mlsanity.com/paper/1612.01452/) (2016)
- [Objective Classes for Micro-Facial Expression Recognition](https://mlsanity.com/paper/1708.07549/) (2017)
- [Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion](https://mlsanity.com/paper/2008.01003/) (2020)
- [Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System](https://mlsanity.com/paper/1904.01748/) (2019)
- [ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis](https://mlsanity.com/paper/2005.04370/) (2020)
- [Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognition](https://mlsanity.com/paper/2005.07991/) (2020)
- [Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning](https://mlsanity.com/paper/2008.05563/) (2020)
- [On Improving the Generalization of Face Recognition in the Presence of Occlusions](https://mlsanity.com/paper/2006.06787/) (2020)

## Abstract (arXiv)

Micro-expression recognition (MER) has attracted lots of researchers' attention in a decade. However, occlusion will occur for MER in real-world scenarios. This paper deeply investigates an interesting but unexplored challenging issue in MER, ie, occlusion MER. First, to research MER under real-world occlusion, synthetic occluded micro-expression databases are created by using various mask for the community. Second, to suppress the influence of occlusion, a underlineRegion-inspired underlineRelation underlineReasoning underlineNetwork (RRRN) is proposed to model relations between various facial regions. RRRN consists of a backbone network, the Region-Inspired (RI) module and Relation Reasoning (RR) module. More specifically, the backbone network aims at extracting feature representations from different facial regions, RI module computing an adaptive weight from the region itself based on attention mechanism with respect to the unobstructedness and importance for suppressing the influence of occlusion, and RR module exploiting the progressive interactions among these regions by performing graph convolutions. Experiments are conducted on handout-database evaluation and composite database evaluation tasks of MEGC 2018 protocol. Experimental results show that RRRN can significantly explore the importance of facial regions and capture the cooperative complementary relationship of facial regions for MER. The results also demonstrate RRRN outperforms the state-of-the-art approaches, especially on occlusion, and RRRN acts more robust to occlusion.