Distilling Resolution-robust Identity Knowledge for Texture-Enhanced Face Hallucination

Qiqi Bao, Rui Zhu, Bowen Gang, Pengyang Zhao, Wenming Yang, Qingmin Liao

Abstract: The main focus of most existing face hallucination methods is to generate visually pleasing results. However, in many applications, the final goal is to identify the person in the low-resolution (LR) image. In this paper, we propose a texture and identity integration network (TIIN) to effectively incorporate identity information into face hallucination tasks. TIIN consists of an identity-preserving denormalization module (IDM) and an equalized texture enhance module (ETEM). The IDM exploits the identity prior and the ETEM improves image quality through histogram equalization. To extract identity information effectively, we propose a resolution-robust identity knowledge distillation network (RIKDN). RIKDN is specifically designed for LR face recognition and can be of independent interest. It employs two teacher-student streams. One stream narrows the performance gap between high-resolution (HR) and LR images. The other distills correlation information from the HR-HR teacher stream to guide learning in the LR-HR student stream. We conduct extensive experiments on multiple datasets to demonstrate the effectiveness of our methods.