mlsanityData to 7 Oct 2026

Weaknesses of Facial Emotion Recognition Systems

arXiv 2601.12402 ↗First posted 18 Jan 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Emotion detection from faces is one of the machine learning problems needed for human-computer interaction. The variety of methods used is enormous, which motivated an in-depth review of articles and scientific studies. Three of the most interesting and best solutions are selected, followed by the selection of three datasets that stood out for the diversity and number of images in them. The selected neural networks are trained, and then a series of experiments are performed to compare their performance, including testing on different datasets than a model was trained on. This reveals weaknesses in existing solutions, including differences between datasets, unequal levels of difficulty in recognizing certain emotions and the challenges in differentiating between closely related emotions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 18 Jan 2026.

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References · 8

  1. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  2. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
  3. 2018Generalisation in humans and deep neural networkscited by 237
  4. 2021Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognitioncited by 89
  5. 2021Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognitioncited by 55
  6. 2016From Facial Expression Recognition to Interpersonal Relation Predictioncited by 53
  7. 2022POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognitioncited by 49
  8. 2025Object Recognition Datasets and Challenges: A Reviewcited by 2

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