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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • The COVID-19 pandemic highlighted the challenge masked faces pose to existing face recognition systems.
  • Effective face recognition is critical for security and public health applications.
  • Standard systems struggle with occlusions like face masks.

Purpose of the Study:

  • To develop a robust masked face recognition method resilient to mask occlusions.
  • To enhance the performance of face recognition systems in real-world scenarios with masks.
  • To investigate training strategies for recognizing both masked and unmasked faces.

Main Methods:

  • A novel approach integrating an optimal cropping strategy with the Convolutional Block Attention Module (CBAM).
  • CBAM focuses on discriminative facial regions, particularly around the eyes.
  • Exploration of two training scenarios: training on unmasked faces to recognize masked faces, and vice-versa.

Main Results:

  • The proposed method significantly improves masked face recognition accuracy.
  • Experiments conducted on multiple benchmark datasets (SMFRD, CISIA-Webface, AR, Extend Yela B) validate the approach.
  • The integration of cropping and CBAM demonstrates superior performance over existing methods.

Conclusions:

  • The developed method offers a significant advancement in masked face recognition technology.
  • This approach is effective for handling occlusions caused by face masks in various scenarios.
  • The findings contribute to more reliable biometric systems in the post-pandemic era.