Sparsity-Inducing Divergence Losses for Biometric Verification

Performance in face and speaker verification is largely drivenby margin-penalty softmax losses such as CosFace and ArcFace. Recentlyintroduced α-divergence loss functions offer a compelling alternative, par-ticularly due to their ability to induce sparse solutions (when α > 1).However, standard geometric margins are designed for the softmax func-tion and do not naturally extend to this generalized probabilistic frame-work. In this paper we propose Q-Margin, a novel α-divergence lossthat introduces a principled probabilistic margin. Unlike conventionalmethods that apply geometric penalties to the logits (unnormalized log-likelihoods), Q-Margin encodes the margin penalty directly into the ref-erence measure (prior probabilities). This formulation naturally encour-ages discriminative embeddings while preserving the beneficial sparsityproperties of the α-divergence. We demonstrate that Q-Margin achievescompetitive or superior performance on the challenging IJB-B and IJB-C face verification benchmarks and similarly strong results in speakerverification on VoxCeleb. Crucially, against ArcFace and CosFace base-lines trained under an identical recipe, Q-Margin consistently improvesat low False Acceptance Rates (FARs), a capability critical for practicalhigh-security applications. Finally, the extreme sparsity of the Q-Marginposteriors enables exact and memory-efficient training, offering a scalablesolution for datasets with millions of identities.