Abstract
In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared with existing labelled face datasets, a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through self-supervised pretraining to transfer generalized face recognition performance. Moreover, motivated by one recent finding, that is, the face saliency area is critical for face recognition, in contrast to utilizing random cropped blocks of images for constructing augmentations in pretraining, we utilize patches localized by extracted facial landmarks. This enables our method - namely LAndmark-based Facial Self-supervised learning LAFS), to learn key representation that is more critical for face recognition. We also incorporate two landmark-specific augmentations which introduce more diversity of landmark information to further regularize the learning. With learned landmark-based facial representations, we further adapt the representation for face recognition with regularization mitigating variations in landmark positions. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks, especially on more challenging few-shot scenarios.
Abstract (translated)
在这项工作中，我们专注于学习可以在训练有效面部识别模型时进行自适应的面部表示。特别是在没有标签的情况下。首先，与现有的带有标签的人脸数据集相比，现实世界中存在大量未标记的脸。我们通过自监督预训练来探索这些未标记面部图像的学习策略，以转移通用的面部识别性能。此外，受到最近的一个发现的影响，即脸部显著区域对于面部识别至关重要，我们使用通过提取面部特征点来定位的补丁作为自监督学习中的标签。这使得我们的方法 - 基于LAndmark的特征点自监督学习LAFS） - 可以学习更具关键性的面部表示。我们还引入了两种特定特征点的自监督增强，以进一步规范化学习。通过学习基于特征点的面部表示，我们进一步通过正则化来缓解特征点位置的变化。我们的方法在多个面部识别基准测试中都实现了显著的改进，尤其是在更具挑战性的几拍场景中。
URL
https://arxiv.org/abs/2403.08161
