Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition
This work addresses face recognition challenges under occlusion and varying view angles, offering an incremental improvement in template efficiency and performance for image-set based systems.
The paper tackles the problem of redundant information in concatenated deep network representations for face recognition by proposing an occlusion-guided compact template learning approach that uses only visible facial patches, resulting in significantly better verification performance with a template size an order-of-magnitude smaller than previous methods.
Concatenation of the deep network representations extracted from different facial patches helps to improve face recognition performance. However, the concatenated facial template increases in size and contains redundant information. Previous solutions aim to reduce the dimensionality of the facial template without considering the occlusion pattern of the facial patches. In this paper, we propose an occlusion-guided compact template learning (OGCTL) approach that only uses the information from visible patches to construct the compact template. The compact face representation is not sensitive to the number of patches that are used to construct the facial template and is more suitable for incorporating the information from different view angles for image-set based face recognition. Instead of using occlusion masks in face matching (e.g., DPRFS [38]), the proposed method uses occlusion masks in template construction and achieves significantly better image-set based face verification performance on a challenging database with a template size that is an order-of-magnitude smaller than DPRFS.