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Enhanced Sample Self-Revised Network for Cross-Dataset Facial Expression Recognition.

Xiaolin Xu1,2, Yuan Zong1,2, Cheng Lu1

  • 1Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing 210096, China.

Entropy (Basel, Switzerland)
|July 8, 2023
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Summary
This summary is machine-generated.

This study introduces an enhanced sample self-revised network (ESSRN) to address outlier samples in cross-dataset facial expression recognition (FER). The novel mechanism effectively suppresses outliers, improving FER performance across diverse datasets.

Keywords:
cross-dataset facial expression recognitionfacial expression recognitiontransfer learningunsupervised domain adaptation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cross-dataset facial expression recognition (FER) is crucial but hindered by outlier samples in large datasets.
  • Outliers, caused by low quality, subjective labels, occlusion, or rare identities, distort feature distributions.
  • These distortions negatively impact the performance of most cross-dataset FER methods.

Purpose of the Study:

  • To propose an enhanced sample self-revised network (ESSRN) for effective outlier handling in cross-dataset FER.
  • To develop a novel mechanism to identify and suppress outlier samples.
  • To improve the robustness and accuracy of facial expression recognition across different datasets.

Main Methods:

  • Developed the Enhanced Sample Self-Revised Network (ESSRN).
  • Implemented a novel outlier-handling mechanism within the ESSRN.
  • Conducted extensive cross-dataset experiments on RAF-DB, JAFFE, CK+, and FER2013.

Main Results:

  • The proposed outlier-handling mechanism effectively reduces the negative impact of outlier samples.
  • ESSRN demonstrates superior performance compared to classic unsupervised domain adaptation (UDA) methods.
  • The method achieves state-of-the-art results in cross-dataset facial expression recognition.

Conclusions:

  • The ESSRN with its outlier-handling mechanism is effective for cross-dataset FER.
  • The proposed approach significantly mitigates the performance degradation caused by outlier samples.
  • This work advances the field of robust cross-dataset facial expression recognition.