Enhancing open-set face recognition by closing it with Cluster-Inferred Gallery Augmentation

Abstract

In open-set face recognition - as opposed to closed-set face recognition - it is possible that the identity of a given query is not present in the gallery set. In that case, the identity of the query can only be correctly classified as "unknown" when the similarity with the gallery faces is below a threshold that was determined a priori. However, in many use-cases, the set of queries contains multiple instances of the same identity, whether or not this identity is represented in the gallery. Thus, the set of query faces lends itself to identity clustering that could yield representative instances for unknown identities. By augmenting the gallery with these instances, we can make an open-set face recognition problem more closed. In this paper, we show that this method of Cluster-Inferred Gallery Augmentation (CIGA) does indeed improve the quality of open-set face recognition. We evaluate the addition of CIGA for both a private dataset of images taken in a school context and the public LFW dataset, showing a significant improvement in both cases. Moreover, an implementation of the suggested approach along with our experiments are made publicly available on https://gitlab.com/florisdf/acpr2019.status: publishe