AggNet: Learning to aggregate faces for group membership verification

Abstract

In certain applications of face recognition, our goal is to verify whether an individual belongs to a particular group while keeping their identity undisclosed. Existing methods have suggested a process of quantizing pre-computed face descriptors into discrete embeddings and aggregating them into a single representation for the group. However, this mechanism is only optimized for a given closed set of individuals and requires relearning the group representations from scratch whenever the groups change. In this paper, we introduce a deep architecture that simultaneously learns face descriptors and the aggregation mechanism to enhance overall performance. Our system can be utilized for new groups comprising individuals who have never been encountered before, and it easily handles new memberships or the termination of existing memberships. Through experiments conducted on multiple extensive, real-world face datasets, we demonstrate that our proposed method achieves superior verification performance compared to other baseline approaches.