Ring loss: Convex Feature Normalization for Face Recognition
This addresses the challenge of robust face recognition for applications like security and biometrics, representing an incremental improvement over existing normalization techniques.
The paper tackles the problem of deep feature normalization in supervised classification by introducing Ring loss, a convex soft normalization method that constrains feature norms to a scaled unit circle, achieving state-of-the-art performance on face recognition benchmarks like IJB-A Janus and Janus CS3.
We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax. We argue that deep feature normalization is an important aspect of supervised classification problems where we require the model to represent each class in a multi-class problem equally well. The direct approach to feature normalization through the hard normalization operation results in a non-convex formulation. Instead, Ring loss applies soft normalization, where it gradually learns to constrain the norm to the scaled unit circle while preserving convexity leading to more robust features. We apply Ring loss to large-scale face recognition problems and present results on LFW, the challenging protocols of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss outperforms strong baselines, matches state-of-the-art performance on IJB-A Janus and outperforms all other results on the challenging Janus CS3 thereby achieving state-of-the-art. We also outperform strong baselines in handling extremely low resolution face matching.