Although deep learning has significantly improved Face Recognition (FR),
dramatic performance deterioration may occur when processing Low Resolution
(LR) faces. To alleviate this, approaches based on unified feature space are
proposed with the sacrifice under High Resolution (HR) circumstances. To deal
with the huge domain gap between HR and LR domains and achieve the best on both
domains, we first took a closer look at the impacts of several resolution
augmentations and then analyzed the difficulty of LR samples from the
perspective of the model gradient produced by different resolution samples.
Besides, we also find that the introduction of some resolutions could help the
learning of lower resolutions. Based on these, we divide the LR samples into
three difficulties according to the resolution and propose a more effective
Multi-Resolution Augmentation. Then, due to the rapidly increasing domain gap
as the resolution decreases, we carefully design a novel and effective metric
loss based on a LogExp distance function that provides decent gradients to
prevent oscillation near the convergence point or tolerance to small distance
errors; it could also dynamically adjust the penalty for errors in different
dimensions, allowing for more optimization of dimensions with large errors.
Combining these two insights, our model could learn more general knowledge in a
wide resolution range of images and balanced results can be achieved by our
extremely simple framework. Moreover, the augmentations and metrics are the
cornerstones of LRFR, so our method could be considered a new baseline for the
LRFR task. Experiments on the LRFR datasets: SCface, XQLFW, and large-scale
LRFR dataset: TinyFace demonstrate the effectiveness of our methods, while the
degradation on HRFR datasets is significantly reduced.

研究提出一种新的处理低分辨率人脸识别的框架，其中采用了多分辨率增强和基于对数指数距离函数的度量损失，能够有效克服低分辨率下表现退化的问题，并在多种实验数据集上表现出良好的识别性能。