mlsanityData to 7 Oct 2026

Facial Emotion Recognition: State of the Art Performance on FER2013

arXiv 2105.03588 ↗First posted 8 May 2021cs.CV cs.AI cs.LGcited by 20

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Abstract (arXiv)

Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techniques for FER, deep learning models, especially Convolutional Neural Networks (CNNs) have shown great potential due to their powerful automatic feature extraction and computational efficiency. In this work, we achieve the highest single-network classification accuracy on the FER2013 dataset. We adopt the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods. To our best knowledge, our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 16 of its 37 references that are in the map. First posted on arXiv 8 May 2021.

Builds on

16 of 37 references.

References · 16

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4 more
  1. 2018Local Learning with Deep and Handcrafted Features for Facial Expression Recognitioncited by 23
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 4; 2025: 6; 2026: 2.

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  1. 2024Revisiting Weight Averaging for Model Mergingcited by 24
  2. 2025ModernVBERT: Towards Smaller Visual Document Retrieverscited by 19
  3. 2021Local Multi-Head Channel Self-Attention for Facial Expression Recognitioncited by 7
  4. 2025EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognitioncited by 4
  5. 2022A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluationcited by 3
  6. 2024Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with Peoplecited by 3
  7. 2021Product Re-identification System in Fully Automated Defect Detectioncited by 2
  8. 2022Explore the Expression: Facial Expression Generation using Auxiliary Classifier Generative Adversarial Networkcited by 2
  9. 2022FExGAN-Meta: Facial Expression Generation with Meta Humanscited by 1
  10. 2025Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognitioncited by 1
  11. 2022Adaptive Discriminative Regularization for Visual Classificationcited by 0
  12. 2023The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learningcited by 0
8 more
  1. 2023Facial Emotion Recognition in VR Gamescited by 0
  2. 2024Target-Dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-Caption Translation Network using Visual-Caption Pairscited by 0
  3. 2024Emotion Recognition with Facial Attention and Objective Activation Functionscited by 0
  4. 2025Emotion estimation from video footage with LSTMcited by 0
  5. 2025Achieving 3D Attention via Triplet Squeeze and Excitation Blockcited by 0
  6. 2025Evaluating Open-Source Vision Language Models for Facial Emotion Recognition against Traditional Deep Learning Modelscited by 0
  7. 2026Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detectioncited by 0
  8. 2026Does Model Uncertainty Track Human Ambiguity? Evidence from Multi-Annotator Vision Benchmarkscited by 0