Recent deep learning based face recognition methods have achieved great
performance, but it still remains challenging to recognize very low-resolution
query face like 28x28 pixels when CCTV camera is far from the captured subject.
Such face with very low-resolution is totally out of detail information of the
face identity compared to normal resolution in a gallery and hard to find
corresponding faces therein. To this end, we propose a Resolution Invariant
Model (RIM) for addressing such cross-resolution face recognition problems,
with three distinct novelties. First, RIM is a novel and unified deep
architecture, containing a Face Hallucination sub-Net (FHN) and a Heterogeneous
Recognition sub-Net (HRN), which are jointly learned end to end. Second, FHN is
a well-designed tri-path Generative Adversarial Network (GAN) which
simultaneously perceives facial structure and geometry prior information, i.e.
landmark heatmaps and parsing maps, incorporated with an unsupervised
cross-domain adversarial training strategy to super-resolve very low-resolution
query image to its 8x larger ones without requiring them to be well aligned.
Third, HRN is a generic Convolutional Neural Network (CNN) for heterogeneous
face recognition with our proposed residual knowledge distillation strategy for
learning discriminative yet generalized feature representation. Quantitative
and qualitative experiments on several benchmarks demonstrate the superiority
of the proposed model over the state-of-the-arts. Codes and models will be
released upon acceptance.

该研究提出了一个统一的分辨率不变模型（RIM）来解决低分辨率人脸识别问题，其中包含面部幻觉子网络（FHN）和异构识别子网络（HRN）。FHN 是一个三路生成对抗网络（GAN），使用无监督跨域对抗训练策略将非常低的分辨率查询图像超分辨率到比其 8 倍大的图像，HRN 是一个泛用的卷积神经网络（CNN），用于具有提取判别性特征表示的残差知识蒸馏策略的异构人脸识别。