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Facial Emotion Recognition: State of the Art Performance on FER2013

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arxiv 2105.03588 v1 pith:AOG5VMNB submitted 2021-05-08 cs.CV cs.AIcs.LG

Yousif Khaireddin , Zhuofa Chen This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords facialfer2013accuracyemotionmodelsrecognitionsingle-networkaccurate
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Revisiting Weight Averaging for Model Merging

    cs.LG 2024-12 conditional novelty 6.0 of 10

    Centering task vectors around the weight average and keeping their top singular vectors yields a merged multi-task model that outperforms prior merging methods on vision and NLP benchmarks.

  2. Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition

    cs.CV 2025-02 reject novelty 4.0 of 10

    Milmer reports 96.72% four-class accuracy on DEAP by fusing facial frames selected via multiple instance learning with EEG tokens in a transformer, but the evaluation protocol is not fully described.

  3. EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition

    cs.CV 2025-01 conditional novelty 4.0 of 10

    EmoNeXt, a ConvNeXt variant using spatial transformers, squeeze-and-excitation blocks, and a self-attention variance regularizer, reports 76.12% accuracy on FER2013.

  4. A Trustworthy Method for Multimodal Emotion Recognition

    cs.CV 2025-08 reject novelty 3.0 of 10

    A confidence-based multimodal emotion recognition system built from an existing trusted classification method reports high trusted F1 scores using a new evaluation metric whose threshold is fitted to the test set.

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