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<p>Facial expression recognition (FER) using artificial intelligence remains a challenging task due to data limitations and intra-class variations. This study proposes a novel deep learning framework that synergizes convolutional neural networks (CNNs) for classification with generative adversarial networks (GANs) for data augmentation, effectively addressing data scarcity and enhancing model generalization. The integration of GANs enables the generation of high-fidelity synthetic images, mitigating overfitting and improving classification robustness. Experimental validation on the FER2013 dataset demonstrates the efficacy of the proposed approach, achieving superior accuracy compared to conventional methods. This work contributes to advancing FER by enhancing recognition performance and establishing a scalable solution applicable to diverse facial expression datasets.</p>

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Facial expression recognition using CNNs and GANs: a study on classification and generation of synthetic images

  • 期刊论文

  • 发表日期：

    2025年4月24日

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  • Shekhar Singh,
  • Fatma Nasoz

摘要

Facial expression recognition (FER) using artificial intelligence remains a challenging task due to data limitations and intra-class variations. This study proposes a novel deep learning framework that synergizes convolutional neural networks (CNNs) for classification with generative adversarial networks (GANs) for data augmentation, effectively addressing data scarcity and enhancing model generalization. The integration of GANs enables the generation of high-fidelity synthetic images, mitigating overfitting and improving classification robustness. Experimental validation on the FER2013 dataset demonstrates the efficacy of the proposed approach, achieving superior accuracy compared to conventional methods. This work contributes to advancing FER by enhancing recognition performance and establishing a scalable solution applicable to diverse facial expression datasets.