Axial Sphere Loss: Encouraging Open-Space Risk Minimization in Face Identification Tasks

Open-set face recognition challenges biometric systems by requiring them to identify registered subjects while rejecting unregistered individuals. This task is particularly demanding in watchlist scenarios, where biometric systems must focus on subjects of interest and disregard irrelevant faces. To address real-world face applications, this study associates quickly trainable adaptation networks with a logit-and-distance-based cost function that explores non-gallery samples in favor of minimizing the open-space risk. These negative instances are either specified in dataset protocols or synthetically built at training time. The proposed Axial Sphere Loss (ASL) shifts each class into pre-defined regions in the latent space and mutually pushes non-gallery samples toward the space origin, forming spherical containers around each class template at inference time. We show that training an adapter network with ASL does not hinder closed-set recognition scores but significantly boosts open-set identification rates, achieving state-of-the-art performance on three well-known face benchmarks, namely, LFW, IJB-C, and UCCS datasets.