Attribute Augmented Convolutional Neural Network for Face Hallucination

Cheng-Han Lee, Kaipeng Zhang, Hu-Cheng Lee, Chia-Wen Cheng, Winston Hsu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 721-729

Abstract


Though existing face hallucination methods achieve great performance on the global region evaluation, most of them cannot recover local attributes accurately, especially when super-resolving a very low-resolution face image from 14X12 pixels to its 8X larger one. In this paper, we propose a brand new Attribute Augmented Convolutional Neural Network (AACNN) to assist face hallucination by exploiting facial attributes. The goal is to augment face hallucination, particularly the local regions, with informative attribute description. More specifically, our method fuses the advantages of both image domain and attribute domain, which significantly assists facial attributes recovery. Extensive experiments demonstrate that our proposed method achieves superior visual quality of hallucination on both local region and global region against the state-of-the-art methods. In addition, our AACNN still improves the performance of hallucination adaptively with partial attribute input.

Related Material


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[bibtex]
@InProceedings{Lee_2018_CVPR_Workshops,
author = {Lee, Cheng-Han and Zhang, Kaipeng and Lee, Hu-Cheng and Cheng, Chia-Wen and Hsu, Winston},
title = {Attribute Augmented Convolutional Neural Network for Face Hallucination},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}