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Concatenation of the deep network representations extracted from differentfacial patches helps to improve face recognition performance. However, theconcatenated facial template increases in size and contains redundantinformation. Previous solutions aim to reduce the dimensionality of the facialtemplate without considering the occlusion pattern of the facial patches. Inthis paper, we propose an occlusion-guided compact template learning (OGCTL)approach that only uses the information from visible patches to construct thecompact template. The compact face representation is not sensitive to thenumber of patches that are used to construct the facial template and is moresuitable for incorporating the information from different view angles forimage-set based face recognition. Instead of using occlusion masks in facematching (e.g., DPRFS [38]), the proposed method uses occlusion masks intemplate construction and achieves significantly better image-set based faceverification performance on a challenging database with a template size that isan order-of-magnitude smaller than DPRFS.

Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition