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Low-resolution face recognition (LRFR) intends to identify unknown poor-quality face images and is widely employed in real-world surveillance applications. While collecting a large-scale labeled low-resolution (LR) face dataset could be conducive, it is practically infeasible due to labor costs and privacy issues. In contrast, accessing high-resolution (HR) face datasets is relatively effortless. However, prevailing domain adaptation techniques are often tenuous as they demand sharing of similar face images at different resolutions. We propose disjoint-identity resolution adaptation (DIRA) to transfer substantial face semantic representations from HR to LR face images, despite disjoint identities and limited labeled LR images. We accredit that continuous adversarial learning between HR-LR resolution alignment and segregation renders effective feature extraction and discriminative LR face representation. Our experimental results show a notable performance boost over the recent state-of-the-art methods for the challenging realistic low-resolution face recognition task.

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