NormalFace: Rectifying Variance Amplification for Robust Face Recognition

Modern face recognition paradigms predominantly rely on angular margin penalties to enforce strict geometric boundaries between classes. However, these methods suffer from Margin Saturation, where increasing margin penalties yield negligible gains due to an intrinsic performance ceiling. In this work, we identify a statistical contributing mechanism of this bottleneck: the uncontrolled variance growth of negative proxies. Through theoretical analysis, we show that the stochastic noise inherent in SGD training drives negative proxy similarities to converge to Normal distributions with accumulating variance, a critical issue we term the Variance Amplification Defect. This defect increases inter-class confusion, which can be particularly detrimental in high-security scenarios sensitive to tail distributions. To address this, we propose NormalFace, a corrective framework centered on variance-aware loss modulation that requires no additional manually tuned hyperparameters. Unlike heuristic approaches requiring complex hyperparameter searches, NormalFace derives a closed-form weight function that explicitly counteracts the SGD-induced variance drift. Experimental results across multiple benchmarks demonstrate that NormalFace effectively rectifies distribution distortion. While achieving performance on par with strong baselines on standard metrics, it shows particular advantages in stringent high-security regimes (e.g., FAR=1e-6), where controlling false-accept risk is especially important, supporting its role as a useful correction to the softmax-based paradigm.