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Adversarially Learning Occlusions by Backpropagation for Face Recognition.

Caijie Zhao1, Ying Qin1, Bob Zhang1,2

  • 1PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa 999078, Macau SAR, China.

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|October 28, 2023
PubMed
Summary
This summary is machine-generated.

The Adversarially Learning Occlusions by Backpropagation (ALOB) model enhances face recognition for occluded images without manual labeling. This novel approach significantly improves performance on masked and general face recognition tasks.

Keywords:
adversarial learningdeep neural networkend-to-endoccluded face recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep neural networks have advanced face recognition to human-level performance.
  • Current models struggle with occluded faces due to a lack of large-scale, labeled datasets.
  • Manual labeling of occlusions is labor-intensive and costly.

Purpose of the Study:

  • To propose a novel method for robust occluded face recognition.
  • To mitigate the need for manual labeling of occlusions in training data.
  • To improve the performance of face recognition systems in real-world scenarios with occlusions.

Main Methods:

  • Introduced the Adversarially Learning Occlusions by Backpropagation (ALOB) model, a double-network framework.
  • Employed contrastive learning to learn corrupted features against identity labels, maximizing loss.
  • Avoided manual annotation of occlusions through adversarial learning.

Main Results:

  • ALOB demonstrated superior performance in occluded face recognition compared to state-of-the-art methods.
  • Achieved a 100% recognition rate for sunglasses on AR datasets (protocols 1 and 2).
  • Attained high accuracies on LFW-OCC datasets (e.g., 94.87% TAR@FAR = 1 × 10-3 on LFW-OCC-2.0) and generalized well across LFW, LFW-Masked, and MFR2 datasets.

Conclusions:

  • The ALOB model effectively addresses the challenge of occluded face recognition.
  • The proposed method offers a scalable solution by eliminating the need for manual occlusion labeling.
  • ALOB shows strong generalization capabilities for both general and masked face recognition.