Low-resolution face recognition suffers from domain shift due to the different resolution between a high-resolution gallery and a low-resolution probe set. Conventional methods use the pairwise correlation between high-resolution and low-resolution for the same subject, which requires label information for both gallery and probe sets. However, explicitly labeled low-resolution probe images are seldom available, and labeling them is labor-intensive. In this paper, we propose a novel unsupervised face domain transfer for robust low-resolution face recognition. By leveraging the attention mechanism, the proposed generative face augmentation reduces the domain shift at image-level, while spatial resolution adaptation generates domain-invariant and discriminant feature distributions. On public datasets, we demonstrate the complementarity between generative face augmentation at image-level and spatial resolution adaptation at feature-level. The proposed method outperforms the state-of-the-art supervised methods even though we do not use any label information of low-resolution probe set.

中文翻译：

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用于低分辨率人脸识别的无监督人脸域转移

由于高分辨率图库和低分辨率探针集之间的分辨率不同，低分辨率人脸识别会遭受域转移。传统方法对同一主题使用高分辨率和低分辨率之间的成对相关性，这需要画廊和探针集的标签信息。然而，很少有明确标记的低分辨率探测图像，并且标记它们是劳动密集型的。在本文中，我们提出了一种新颖的无监督人脸域转移，用于鲁棒的低分辨率人脸识别。通过利用注意力机制，所提出的生成人脸增强减少了图像级别的域偏移，而空间分辨率自适应生成域不变和判别特征分布。在公共数据集上，我们证明了图像级别的生成面部增强和特征级别的空间分辨率适应之间的互补性。即使我们不使用任何低分辨率探针集的标签信息，所提出的方法也优于最先进的监督方法。