Gicoface: Global Information-Based Cosine Optimal Loss for Deep Face Recognition

Abstract

Loss function plays an important role in CNNs. However, the recent loss functions either do not apply weight and feature normalisation or do not explicitly follow the two targets of improving discriminative ability: minimising intra-class variance and maximising inter-class variance. Besides, all of them consider only the feedback information from the current mini-batch instead of the distribution information from the whole training set. In this paper, we propose a novel loss function -Global Information-based Cosine Optimal loss (Gico loss). Gico loss is applied with weight and feature normalisation, designed explicitly following the aforementioned two targets of improving discriminative ability, and is guided by the distribution information from the whole training set. Extensive experiments are conducted on multiple public datasets, which confirms the effectiveness of the proposed Gico loss and shows that we achieve state-of-the-art performance.

GicoFace: A Deep Face Recognition Model Based on Global-Information Loss Function

“…Experimental results presented in Section 3 confirm the efficacy of the proposed method and show the state-of-the-art performance of the method. [7] No Yes Yes Yes mini-batch SFace loss [12] Yes Yes Yes Yes mini-batch CVM loss [11] Yes Yes No No mini-batch L-Softmax loss [8] No Yes No No mini-batch A-Softmax loss [9] No Yes Yes No mini-batch AM-Softmax loss [10] No Yes Yes Yes mini-batch ArcFace [6] No Please note that an earlier version of this paper [22] was presented at the International Conference on Image Processing. Compared with the earlier version, this journal paper adds about 50% new content: (1) experiments on MegaFace and FaceScrub datasets to further verify the effectiveness of the proposed methods; (2) more detailed description on related works; (3) more discussion on the proposed methods to answer some key scientific questions; (4) more details about the complete algorithm are given.…”