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Unsupervised Domain Adaptation for Facial Expression Recognition Using Generative Adversarial Networks.

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This study introduces an unsupervised domain adaptation method to improve facial expression recognition across datasets. The approach uses generative adversarial networks (GANs) to create synthetic data, enhancing model performance with limited target data.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Convolutional Neural Networks (CNNs) often exhibit poor performance on new facial expression datasets due to variations in feature distribution.
  • This performance drop hinders the generalizability of CNN models in real-world facial expression recognition tasks.

Purpose of the Study:

  • To enhance the cross-dataset accuracy of CNN models for facial expression recognition.
  • To develop an unsupervised domain adaptation method effective for small, unlabelled target datasets.

Main Methods:

  • Trained a generative adversarial network (GAN) on the target dataset to generate synthetic samples.
  • Utilized GAN-generated samples with dynamically assigned pseudolabels for fine-tuning pre-trained CNN models.
  • Applied the method to existing CNN architectures without modification.

Main Results:

  • Demonstrated significant improvements in cross-dataset accuracy for facial expression recognition.
  • Achieved inspiring results across four diverse facial expression recognition datasets.
  • Validated the effectiveness of the GAN-based unsupervised domain adaptation technique.

Conclusions:

  • The proposed unsupervised domain adaptation method effectively addresses the challenge of varying feature distributions across datasets.
  • The GAN-generated data with pseudolabels offers a viable solution for improving CNN performance on unlabelled, small target datasets.
  • This approach provides a flexible and effective strategy for enhancing the robustness of facial expression recognition models.