Cascaded Hallucination-Classification Deep Network for Low-Resolution Face Recognition in the Wild

DOI

10.2991/aiie-16.2016.52How to use a DOI?

Keywords

deep convolutional neural network; face-hallucination; low-resolution face recognition

Abstract

Low-resolution face recognition (LR FR) has become an active research subarea due to its significances for real applications. Conventional low-resolution face recognition approaches meet challenges like noise affection and lack of effective features with LR faces. In this paper, we propose a deep learning method for LR FR. Our convolutional neural network (CNN) model directly learns an end-to-end classification on LR faces. Different from normal CNN for high-resolution (HR) face recognition, ours integrates a lightweight hallucination network mapping LR images into HR ones. Furthermore, we concatenate the hallucination and classification networks so that the training propagation is operated in one model, which largely boosts the performance over basic CNN and separate two-step models. Besides, our model is robust to varying poses and illuminations in the wild, and also portable to embedded system for its memory- and energy-saving features.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zheyu Zhang
AU  - Peter Cheung
PY  - 2016/11
DA  - 2016/11
TI  - Cascaded Hallucination-Classification Deep Network for Low-Resolution Face Recognition in the Wild
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 223
EP  - 226
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.52
DO  - 10.2991/aiie-16.2016.52
ID  - Zhang2016/11
ER  -