Recognizing wild faces is extremely hard as they appear with all kinds of
variations. Traditional methods either train with specifically annotated
variation data from target domains, or by introducing unlabeled target
variation data to adapt from the training data. Instead, we propose a universal
representation learning framework that can deal with larger variation unseen in
the given training data without leveraging target domain knowledge. We firstly
synthesize training data alongside some semantically meaningful variations,
such as low resolution, occlusion and head pose. However, directly feeding the
augmented data for training will not converge well as the newly introduced
samples are mostly hard examples. We propose to split the feature embedding
into multiple sub-embeddings, and associate different confidence values for
each sub-embedding to smooth the training procedure. The sub-embeddings are
further decorrelated by regularizing variation classification loss and
variation adversarial loss on different partitions of them. Experiments show
that our method achieves top performance on general face recognition datasets
such as LFW and MegaFace, while significantly better on extreme benchmarks such
as TinyFace and IJB-S.

本文提出了一种通用表征学习框架来提高人脸识别的性能，包括合成训练数据、分割特征嵌入、用不同自信值调整训练的过程，以及通过正则化分类和对不同划分的变体进行对抗训练损失来减少相关性。实验结果表明，该方法在 LFW 和 Megaface 等人脸识别数据集上取得了最佳性能，而在 Tinyface 和 IJB-S 等极端数据集上表现显著优于其他方法。