A Comprehensive Study on Occlusion Invariant Face Recognition under Face Mask Occlusion
This addresses a critical issue for security and surveillance systems during pandemics, but appears incremental as it builds on existing deep learning approaches.
The paper tackles the problem of face recognition under mask occlusion, which significantly degrades performance, and presents a method that achieves improved results, though specific numbers are not provided in the abstract.
The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face, and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.