On Improving the Generalization of Face Recognition in the Presence of Occlusions

• URL: http://arxiv.org/abs/2006.06787v1
• Date: Thu, 11 Jun 2020 20:17:23 GMT
• Title: On Improving the Generalization of Face Recognition in the Presence of Occlusions
• Authors: Xiang Xu, Nikolaos Sarafianos, Ioannis A. Kakadiaris
• Abstract summary: Occlusion-aware face REcOgnition (OREO) approach learned discriminative facial templates despite the presence of such occlusions. OREO improved the generalization ability of face recognition under occlusions by (10.17%) in a single-image-based setting.
• Score: 13.299431908881425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address a key limitation of existing 2D face recognition methods: robustness to occlusions. To accomplish this task, we systematically analyzed the impact of facial attributes on the performance of a state-of-the-art face recognition method and through extensive experimentation, quantitatively analyzed the performance degradation under different types of occlusion. Our proposed Occlusion-aware face REcOgnition (OREO) approach learned discriminative facial templates despite the presence of such occlusions. First, an attention mechanism was proposed that extracted local identity-related region. The local features were then aggregated with the global representations to form a single template. Second, a simple, yet effective, training strategy was introduced to balance the non-occluded and occluded facial images. Extensive experiments demonstrated that OREO improved the generalization ability of face recognition under occlusions by (10.17%) in a single-image-based setting and outperformed the baseline by approximately (2%) in terms of rank-1 accuracy in an image-set-based scenario.

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