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Dual-Proxy Modeling for Masked Face Recognition.

Wang Shuhui1, Mao Xiaochen1

  • 1Shanghai Institute of Measurement and Testing Technology, 1500 Zhang Heng Road, Shanghai, 201203, P.R.China.

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|November 7, 2022
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Summary
This summary is machine-generated.

This study introduces a dual-proxy training method to enhance face recognition accuracy for masked individuals without compromising performance on unmasked faces. The novel approach addresses the challenge of maintaining high accuracy across both masked and unmasked face recognition scenarios.

Keywords:
Dual-ProxyMasked face recognitiondeep learningneural network

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • The COVID-19 pandemic necessitates daily mask-wearing, significantly degrading conventional face recognition system accuracy.
  • Existing methods using synthetic masked faces often reduce accuracy on unmasked faces, creating a performance conflict.

Purpose of the Study:

  • To develop a face recognition training method that improves performance on masked faces while preserving accuracy on unmasked faces.
  • To resolve the contradiction between masked and unmasked face recognition accuracy.

Main Methods:

  • Proposed a dual-proxy face recognition training method utilizing two fully-connected layers as unmasked and masked feature space proxies.
  • Implemented cross-space constraints to ensure intra-class compactness and inter-class discrepancy between feature spaces.

Main Results:

  • Demonstrated superior performance on masked face benchmarks, including real-world and generated masked faces.
  • Achieved state-of-the-art results on masked face recognition without significant accuracy loss on unmasked faces.

Conclusions:

  • The dual-proxy method effectively enhances masked face recognition accuracy.
  • This approach successfully maintains unmasked face recognition performance, addressing a critical challenge in current systems.