OGCTL: Occlusionguided compact template learning for ensemble deep networkbased poseinvariant face recognition 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 occlusionguided 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 imageset 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 imageset based face verification performance on a challenging database with a template size that is an orderofmagnitude smaller than DPRFS.

Occlusion Deep Alignment Network The performance of face localization system often degenerates when occlusions occur. In this paper, we propose Occlusion Deep Alignment Network(ODAN), which is robust to occlusion. Occlusion detection and rough face alignment are employed simultaneously, where the occlusion detection of landmarks is treated as a regression problem. Based on occlusion information, localization offset scaling is employed to transform the difference between the groundtruth and the rough landmark estimation to another linear space to generate target positions for precise face alignment, reducing the difficulty of the regression task. Finally, the outputs of the proposed model are transformed back to Euclidean Space. Experiments on two public datasets show that the proposed method achieves stateofart performance under severe occlusion both in accuracy and efficiency.