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  3. Towards A Better Performance In Facial Expression Recognition: A Data-centric Approach.
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  3. Towards A Better Performance In Facial Expression Recognition: A Data-centric Approach.

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Towards a Better Performance in Facial Expression Recognition: A Data-Centric Approach.

Christian Mejia-Escobar1, Miguel Cazorla2, Ester Martinez-Martin2

  • 1Central University of Ecuador, P.O. Box 17-03-100, Quito, Ecuador.

Computational Intelligence and Neuroscience
|November 13, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel data-centric approach to improve facial expression recognition by refining datasets. The method enhances model accuracy without altering facial images, achieving state-of-the-art results.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition is crucial for various applications but faces challenges in real-world scenarios.
  • Current research predominantly focuses on model-centric improvements, with insufficient attention to dataset quality.
  • Misclassification in facial image datasets hinders the performance of automatic facial expression recognition systems.

Purpose of the Study:

  • To propose a novel data-centric method to address misclassification issues in facial image datasets.
  • To enhance the accuracy and robustness of facial expression recognition models.
  • To improve the quality of facial expression datasets without modifying or augmenting images.

Main Methods:

  • A data-centric strategy involving progressive dataset refinement through successive training of a fixed Convolutional Neural Network (CNN) model.
  • Utilizing correctly predicted facial images from previous training iterations to progressively refine the dataset.
  • Implementing automatic reclassification of the entire dataset after the final training iteration.

    • Main Results:

    • Significant improvements in validation accuracy on FER2013 (20.45%), NHFI (14.47%), and AffectNet (39.66%).
    • Achieved state-of-the-art recognition rates on reclassified datasets: 86.71% (FER2013), 70.44% (NHFI), and 89.17% (AffectNet).
    • Demonstrated the effectiveness of the data-centric approach without image modification, deletion, or augmentation.

    Conclusions:

    • The proposed data-centric method effectively tackles misclassification in facial expression datasets.
    • This approach enhances facial expression recognition accuracy and achieves state-of-the-art performance.
    • Focusing on dataset quality is a promising direction for advancing facial expression recognition technology.