Additive Orthant Loss for Deep Face Recognition

Abstract

In this paper, we propose a novel loss function for deep face recognition, called the additive orthant loss (Orthant loss), which can be combined for softmax-based loss functions to improve the feature-discriminative capability. The Orthant loss makes features away from the origin using the rescaled softplus function and an additive margin. Additionally, the Orthant loss compresses feature spaces by mapping features to an orthant of each class using element-wise operation and 1-bit quantization. As a consequence, the Orthant loss improves the inter-class separabilty and the intra-class compactness. We empirically show that the ArcFace combined with the Orthant loss further compresses and moves the feature spaces farther away from the origin compared to the original ArcFace. Experimental results show that the new combined loss has the most improved accuracy on CFP-FP, AgeDB-30, and MegaFace testing datasets, among some of the latest loss functions.