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Occlusion-guided compact template learning for ensemble deep network-based pose-invariant 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 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.Comment: Accepted by International Conference on Biometrics (ICB 2019) as an
Oral presentatio