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In this paper, we introduce a new large-scale face dataset named VGGFace2.The dataset contains 3.31 million images of 9131 subjects, with an average of362.6 images for each subject. Images are downloaded from Google Image Searchand have large variations in pose, age, illumination, ethnicity and profession(e.g. actors, athletes, politicians). The dataset was collected with threegoals in mind: (i) to have both a large number of identities and also a largenumber of images for each identity; (ii) to cover a large range of pose, ageand ethnicity; and (iii) to minimize the label noise. We describe how thedataset was collected, in particular the automated and manual filtering stagesto ensure a high accuracy for the images of each identity. To assess facerecognition performance using the new dataset, we train ResNet-50 (with andwithout Squeeze-and-Excitation blocks) Convolutional Neural Networks onVGGFace2, on MS- Celeb-1M, and on their union, and show that training onVGGFace2 leads to improved recognition performance over pose and age. Finally,using the models trained on these datasets, we demonstrate state-of-the-artperformance on all the IARPA Janus face recognition benchmarks, e.g. IJB-A,IJB-B and IJB-C, exceeding the previous state-of-the-art by a large margin.Datasets and models are publicly available.

VGGFace2: A dataset for recognising faces across pose and age