ConeFace: Approximate Pairwise Loss for Face Recognition

The discriminability of learned face features is the key to a successful face recognition algorithm under the open-set protocol. Recent research exploits well-designed loss functions to penalize the angles between the deep features and their class centers for reaching the purpose of minimizing the intra-class variance and achieves a significant increase in recognition accuracy. In this paper, we proposed an approximate pairwise loss (APL) to encourage inter-class separability as well as intra-class compactness. More specifically, we use cones to approximate the location of hard examples in the feature space which replaces the thorny hard-mining step, and the APL is obtained by calculating the angular distance between the features of training samples and the cone and we named our method ConeFace. Moreover, ConeFace can be easily used together with other SOTA methods to improve the performance with negligible computational overhead. Extensive experiments on Labeled Face in the Wild (LFW), Celebrities in Frontal-Profile in the Wild (CFP), AgeDB-30, and MegaFace datasets show the effectiveness of the proposed ConeFace.