In Heterogeneous Face Recognition (HFR), the objective is to match faces
across two different domains such as visible and thermal. Large domain
discrepancy makes HFR a difficult problem. Recent methods attempting to fill
the gap via synthesis have achieved promising results, but their performance is
still limited by the scarcity of paired training data. In practice, large-scale
heterogeneous face data are often inaccessible due to the high cost of
acquisition and annotation process as well as privacy regulations. In this
paper, we propose a new face hallucination paradigm for HFR, which not only
enables data-efficient synthesis but also allows to scale up model training
without breaking any privacy policy. Unlike existing methods that learn face
synthesis entirely from scratch, our approach is particularly designed to take
advantage of rich and diverse facial priors from visible domain for more
faithful hallucination. On the other hand, large-scale training is enabled by
introducing a new federated learning scheme to allow institution-wise
collaborations while avoiding explicit data sharing. Extensive experiments
demonstrate the advantages of our approach in tackling HFR under current data
limitations. In a unified framework, our method yields the state-of-the-art
hallucination results on multiple HFR datasets.

本文提出了一种新的面部幻觉范式，该方法不仅可以实现数据有效的综合，还允许扩大模型培训而不违反任何隐私政策。与现有方法完全从头学习面部合成不同，我们的方法特别设计用于利用可见域内的丰富和多样的面部优先知识以获得更加忠实的幻觉，并引入一种新的联邦学习方案，允许机构之间的合作而避免数据共享。在统一框架下，我们的方法在多个 HFR 数据集上产生了最先进的幻觉结果。