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PLFace: Progressive learning for face recognition with mask bias.

Baojin Huang1, Zhongyuan Wang1, Guangcheng Wang1

  • 1NERCMS, School of Computer Science, Wuhan University, Wuhan 430072, China.

Pattern Recognition
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Summary
This summary is machine-generated.

This study introduces Progressive Learning Loss (PLFace) to improve masked face recognition by addressing bias. PLFace trains models to balance recognition of masked and mask-free faces, enhancing overall performance.

Keywords:
Face recognitionMask biasProgressive learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • The COVID-19 pandemic spurred masked face recognition (MFR) development.
  • Overemphasis on MFR accuracy compromises general face recognition.
  • MFR is better viewed as a bias in face recognition, not a separate task.

Purpose of the Study:

  • To mitigate mask bias in face recognition.
  • To develop a method for balanced recognition of masked and mask-free faces.
  • To propose a novel training strategy for deep face recognition.

Main Methods:

  • Introduced Progressive Learning Loss (PLFace) for deep face recognition.
  • Implemented a progressive training strategy adjusting sample importance.
  • Focused on shrinking mask-free embeddings first, then masked embeddings.

Main Results:

  • PLFace adaptively adjusts the importance of masked/mask-free samples.
  • Early training focuses on mask-free samples; later stages emphasize masked samples.
  • Achieved balanced performance for both masked and mask-free face recognition.

Conclusions:

  • PLFace effectively mitigates mask bias in face recognition.
  • The proposed method demonstrates superiority over existing state-of-the-art approaches.
  • PLFace offers a balanced approach to face recognition in the presence of masks.