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A Peek at Peak Emotion Recognition

arXiv 2205.09791 ↗First posted 19 May 2022cs.CV cs.HC cs.MMcited by 0

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Abstract (arXiv)

Despite much progress in the field of facial expression recognition, little attention has been paid to the recognition of peak emotion. Aviezer et al. [1] showed that humans have trouble discerning between positive and negative peak emotions. In this work we analyze how deep learning fares on this challenge. We find that (i) despite using very small datasets, features extracted from deep learning models can achieve results significantly better than humans. (ii) We find that deep learning models, even when trained only on datasets tagged by humans, still outperform humans in this task.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 19 May 2022.

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References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2017Mask R-CNNcited by 7,277
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  6. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
  7. 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
  8. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 492
  9. 2016Convolutional Pose Machinescited by 325
  10. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 302
  11. 2018Deep Facial Expression Recognition: A Surveycited by 270
  12. 2020MediaPipe Hands: On-device Real-time Hand Trackingcited by 161
6 more
  1. 2016Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distributioncited by 158
  2. 2019SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wildcited by 62
  3. 2021Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognitioncited by 55
  4. 2018Survey on Emotional Body Gesture Recognitioncited by 47
  5. 2017Convolutional neural networks pretrained on large face recognition datasets for emotion classification from videocited by 9
  6. 2021Audio-Visual Evaluation of Oratory Skillscited by 1

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