Generative Adversarial Networks-based Face Hallucination with Identity-Preserving

Chia-Hung Yeh, Daniel Chiu, Li-Wei Kang, Chih-Chung Hsu, Chen Lo

Abstract: This paper presents a novel generative adversarial networks-based face hallucination framework for producing high-resolution face images from very low-resolution (LR) ones. We propose a multi-scale generator architecture with multi-scale loss functions for different upscaling factors and a triplet-based identity preserving loss for extracting multi-scale identity-aware facial representations. Experimental results have verified that our method can well super-resolve very LR face images (e.g., 8×8) quantitatively and qualitatively.