@inproceedings{a68c0255cc8a46cc8b01d75442ea4aca,
title = "Generative Adversarial Networks-based Face Hallucination with Identity-Preserving",
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. ",
author = "Yeh, {Chia Hung} and Daniel Chiu and Kang, {Li Wei} and Hsu, {Chih Chung} and Chen Lo",
note = "Funding Information: This work was supported in part by Ministry of Science and Technology, Taiwan, under the Grants MOST 108-2221-E-003-027-MY3, MOST 108-2218-E-003-002-,andMOST108-2218-E-110-002-. Publisher Copyright: {\textcopyright} 2021 IEEE.; 8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 ; Conference date: 15-09-2021 Through 17-09-2021",
year = "2021",
doi = "10.1109/ICCE-TW52618.2021.9603171",
language = "English",
series = "2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021",
address = "United States",
}