On Improving the Generalization of Face Recognition in the Presence of Occlusions
This addresses a key limitation in face recognition for applications like security or surveillance, though it is incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of improving face recognition robustness to occlusions by proposing the OREO approach, which increased generalization ability by 10.17% in single-image settings and outperformed baselines by about 2% in rank-1 accuracy for image-set scenarios.
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.