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 intra-class 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.

本文介绍了使用合成人脸图像的方法解决现实世界人脸识别中困难的问题，并提出了 SynFace，探讨了训练人工合成和真实图像的最新人脸识别模型之间的性能差距及其原因，并提出了 identity mixup 和 domain mixup 方法来缓解性能差距。此外，对于人工合成的图像，本文还对姿态，表情，光照等因素进行了实验分析，提供了几种有效利用合成数据进行人脸识别的方法。

SynFace: 用合成数据进行人脸识别

SynFace: Face Recognition with Synthetic Data

Recent works on domain adaptation reveal the effectiveness of adversarial
learning on filling the discrepancy between source and target domains. However,
two common limitations exist in current adversarial-learning-based methods.
First, samples from two domains alone are not sufficient to ensure
domain-invariance at most part of latent space. Second, the domain
discriminator involved in these methods can only judge real or fake with the
guidance of hard label, while it is more reasonable to use soft scores to
evaluate the generated images or features, i.e., to fully utilize the
inter-domain information. In this paper, we present adversarial domain
adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a
more continuous latent space and guides the domain discriminator in judging
samples' difference relative to source and target domains. Domain mixup is
jointly conducted on pixel and feature level to improve the robustness of
models. Extensive experiments prove that the proposed approach can achieve
superior performance on tasks with various degrees of domain shift and data
complexity.

本研究提出在深度神经网络中结合域随机映射 (Domain Mixup) 的方法用于域自适应领域，研究表明该方法在如处理不同程度的域漂移和数据复杂度的任务中具有卓越的性能。