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Emotion Recognition with Facial Attention and Objective Activation Functions

arXiv 2410.17740 ↗First posted 23 Oct 2024cs.CV cs.AIcited by 0

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Abstract (arXiv)

In this paper, we study the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM, to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV2 to perform the Facial Emotion Recognition task. We show that not only attention can significantly improve the performance of these models but also that combining them with a different activation function can further help increase the performance of these models.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 23 Oct 2024.

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References · 10

  1. 2017Squeeze-and-Excitation Networkscited by 4,763
  2. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  3. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  4. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  5. 2016Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfercited by 1,059
  6. 2017Self-Normalizing Neural Networkscited by 591
  7. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 572
  8. 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 271
  9. 2021YOLO5Face: Why Reinventing a Face Detectorcited by 44
  10. 2021Facial Emotion Recognition: State of the Art Performance on FER2013cited by 20

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