SynFace: Face Recognition with Synthetic Data

With the recent success of deep neural networks, remarkable progress has been achieved on face recognition. However, collecting large-scale real-world training data for face recognition has turned out to be challenging, especially due to the label noise and privacy issues. Meanwhile, existing face r...

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Bibliographic Details
Published in2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 10860 - 10870
Main Authors Qiu, Haibo, Yu, Baosheng, Gong, Dihong, Li, Zhifeng, Liu, Wei, Tao, Dacheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2021
Subjects
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Computer vision
Data privacy
Datasets and evaluation
Deep learning
Face recognition
Faces
Neural networks
Training
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Summary:With the recent success of deep neural networks, remarkable progress has been achieved on face recognition. However, collecting large-scale real-world training data for face recognition has turned out to be challenging, especially due to the label noise and privacy issues. Meanwhile, existing face recognition datasets are usually collected from web images, lacking detailed annotations on attributes (e.g., pose and expression), so the influences of different attributes on face recognition have been poorly investigated. In this paper, we address the above-mentioned issues in face recognition using synthetic face images, i.e., SynFace. Specifically, we first explore the performance gap between recent state-of-the-art face recognition models trained with synthetic and real face images. We then analyze the underlying causes behind the performance gap, e.g., the poor intraclass variations and the domain gap between synthetic and real face images. Inspired by this, we devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the above performance gap, demonstrating the great potentials of synthetic data for face recognition. Furthermore, with the controllable face synthesis model, we can easily manage different factors of synthetic face generation, including pose, expression, illumination, the number of identities, and samples per identity. Therefore, we also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
ISSN:2380-7504
DOI:10.1109/ICCV48922.2021.01070