mlsanityData to 14 Oct 2026

A Perception CNN for Facial Expression Recognition

arXiv 2512.06422 ↗First posted 6 Dec 2025cs.CVcited by 1

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Abstract (arXiv)

Convolutional neural networks (CNNs) can automatically learn data patterns to express face images for facial expression recognition (FER). However, they may ignore effect of facial segmentation of FER. In this paper, we propose a perception CNN for FER as well as PCNN. Firstly, PCNN can use five parallel networks to simultaneously learn local facial features based on eyes, cheeks and mouth to realize the sensitive capture of the subtle changes in FER. Secondly, we utilize a multi-domain interaction mechanism to register and fuse between local sense organ features and global facial structural features to better express face images for FER. Finally, we design a two-phase loss function to restrict accuracy of obtained sense information and reconstructed face images to guarantee performance of obtained PCNN in FER. Experimental results show that our PCNN achieves superior results on several lab and real-world FER benchmarks: CK+, JAFFE, FER2013, FERPlus, RAF-DB and Occlusion and Pose Variant Dataset. Its code is available at https://github.com/hellloxiaotian/PCNN.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 83 references that are in the map. First posted on arXiv 6 Dec 2025.

Builds on

28 of 83 references.

References · 28

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  10. 2020Suppressing Uncertainties for Large-Scale Facial Expression Recognitioncited by 157
  11. 2019Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognitioncited by 127
  12. 2021Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognitioncited by 89
16 more
  1. 2022Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognitioncited by 67
  2. 2020k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)cited by 56
  3. 2016From Facial Expression Recognition to Interpersonal Relation Predictioncited by 53
  4. 2021Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognitioncited by 49
  5. 2022POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognitioncited by 49
  6. 2016Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applicationscited by 45
  7. 2023From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videoscited by 39
  8. 2022Vision Transformer with Attentive Pooling for Robust Facial Expression Recognitioncited by 35
  9. 2022Learning Where to Learn in Cross-View Self-Supervised Learningcited by 31
  10. 2016A Comprehensive Analysis of Deep Learning Based Representation for Face Recognitioncited by 23
  11. 2022Pose-disentangled Contrastive Learning for Self-supervised Facial Representationcited by 17
  12. 2020BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradientcited by 13
  13. 2020Landmark Guidance Independent Spatio-channel Attention and Complementary Context Information based Facial Expression Recognitioncited by 12
  14. 2021"Excavating AI" Re-excavated: Debunking a Fallacious Account of the JAFFE Datasetcited by 12
  15. 2020Point Adversarial Self Mining: A Simple Method for Facial Expression Recognitioncited by 11
  16. 2024Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognitioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A cross-modal network for facial expression recognitioncited by 0