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In recent years, significant progress has been made in face recognition,which can be partially attributed to the availability of large-scale labeledface datasets. However, since the faces in these datasets usually containlimited degree and types of variation, the resulting trained models generalizepoorly to more realistic unconstrained face datasets. While collecting labeledfaces with larger variations could be helpful, it is practically infeasible dueto privacy and labor cost. In comparison, it is easier to acquire a largenumber of unlabeled faces from different domains, which could be used toregularize the learning of face representations. We present an approach to usesuch unlabeled faces to learn generalizable face representations, where weassume neither the access to identity labels nor domain labels for unlabeledimages. Experimental results on unconstrained datasets show that a small amountof unlabeled data with sufficient diversity can (i) lead to an appreciable gainin recognition performance and (ii) outperform the supervised baseline whencombined with less than half of the labeled data. Compared with thestate-of-the-art face recognition methods, our method further improves theirperformance on challenging benchmarks, such as IJB-B, IJB-C and IJB-S.

Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data