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Masked face recognition with convolutional neural networks and local binary patterns.

Hoai Nam Vu1, Mai Huong Nguyen2, Cuong Pham1

  • 1Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, 12110 Vietnam.

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Summary
This summary is machine-generated.

This study introduces a novel masked face recognition method combining deep learning with Local Binary Pattern (LBP) features. The approach effectively recognizes masked faces, achieving high accuracy on custom and public datasets.

Keywords:
Face recognitionLocal binary patternMasked face recognition

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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Face recognition is a common biometric method.
  • The COVID-19 pandemic necessitates mask-wearing, complicating face recognition.
  • Existing methods struggle with occluded facial features.

Purpose of the Study:

  • To develop an effective masked face recognition system.
  • To combine deep learning with Local Binary Pattern (LBP) features for enhanced accuracy.
  • To introduce a new dataset (COMASK20) for masked face recognition research.

Main Methods:

  • Utilized RetinaFace as a deep learning encoder for face detection.
  • Extracted Local Binary Pattern (LBP) features from facial regions (eyes, forehead, eyebrows).
  • Integrated LBP features with RetinaFace-derived features into a unified recognition framework.

Main Results:

  • Achieved an 87% f1-score on the COMASK20 dataset.
  • Achieved a 98% f1-score on the Essex dataset.
  • Outperformed established methods like Dlib and InsightFace.

Conclusions:

  • The proposed method demonstrates high effectiveness and suitability for masked face recognition.
  • The combination of deep learning and LBP features is a promising approach.
  • The COMASK20 dataset provides a valuable resource for future research.