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

arXiv 2107.05904 ↗First posted 13 Jul 2021cs.CV cs.AIcited by 0

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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.

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.

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23 of 68 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2019Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognitioncited by 127
  4. 2018OFF-ApexNet on Micro-expression Recognition Systemcited by 43
  5. 2019Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognitioncited by 41
  6. 2016Less is More: Micro-expression Recognition from Video using Apex Framecited by 32
  7. 2020Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognitioncited by 31
  8. 2018Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognitioncited by 27
  9. 2019Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressionscited by 25
  10. 2019LEARNet Dynamic Imaging Network for Micro Expression Recognitioncited by 22
  11. 2020MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Networkcited by 21
  12. 2014Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changescited by 19
11 more
  1. 2019Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networkscited by 18
  2. 2020Occlusion-Adaptive Deep Network for Robust Facial Expression Recognitioncited by 17
  3. 2018A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challengescited by 16
  4. 2016ImageNet pre-trained models with batch normalizationcited by 14
  5. 2017Objective Classes for Micro-Facial Expression Recognitioncited by 14
  6. 2020Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusioncited by 11
  7. 2019Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition Systemcited by 7
  8. 2020ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesiscited by 7
  9. 2020Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognitioncited by 7
  10. 2020Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learningcited by 7
  11. 2020On Improving the Generalization of Face Recognition in the Presence of Occlusionscited by 4

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