Improve Cross-Domain Face Recognition with IBN-block

Abstract

Domain adaptation is one of the major challenges for face recognition (FR). Most large-scale FR training datasets are built from massive images crawled from the Internet, while in practical applications face images come from specific scenarios. Especially, for applications like ID card verification, registered face images are taken in controlled environments while probe face images are not. There are different distributions between source domain and target domain. In this paper, we propose to use Instance-Batch-Normalization (IBN) block to improve cross-domain FR performance. A million-scale cross-domain test set named IDCard-Scene-1M is used for evaluation. CNN models are trained with an improved loss function which we call L2-ASoftmax Loss. Without using any data from the target domain, IBN-block increased recall rates (@FPR = 10−6) on IDCard-Scene-1M by 1 percentage point for different CNN models. Besides, experiments show that the proposed IBN-CNN models trained with L2-ASoftmax Loss made state-of-the-art performance on MegaFace evaluation.