>State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild (LFW) dataset. However, these models are trained on large-scale datasets that contain millions of real human face images collected from the internet. Web-crawled face images are severely biased (in terms of race, lighting, make-up, etc) and often contain labeling noise. Most importantly, these face images are collected without explicit consent, raising more pressing privacy and ethical concerns. To avoid the problems associated with real face datasets, we introduce a large-scale synthetic dataset for face recognition, obtained by photo-realistic rendering of diverse and high-quality digital faces using a computer graphics pipeline. We compare our method to SynFace, a recent method trained on GAN-generated synthetic faces, and reduce the error rate on LFW by 52.5% (accuracy from 91.93% to 96.17%). We first demonstrate that aggressive data augmentation can significantly help reduce the domain-gap between our synthetic faces and real face images. Taking advantage of having full control over the rendering pipeline, we also study how each attribute (e.g., variation in facial pose, accessories, and textures) affects the accuracy. Finally, by fine-tuning the network on a smaller number of real face images that could reasonably be obtained with consent, we achieve accuracy that is comparable to the methods trained on millions of real face images, while alleviating the problems associated with large datasets. microsoft.github.io
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>video presentation: youtube.com
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>paper: arxiv.org