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Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Low-Rank Representation and Locality-Constrained Regression for Robust Low-Resolution Face Recognition

Authors : Guangwei Gao, Pu Huang, Quan Zhou, Zangyi Hu, Dong Yue

Published in: Artificial Intelligence and Robotics

Publisher: Springer International Publishing

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Abstract

In this paper, we propose a low-rank representation and locality-constrained regression (LLRLCR) based approach to learn the occlusion-robust discriminative representations features for low-resolution face recognition tasks. For gallery set, LLRLCR uses double low-rank representation to reveal the underlying data structures; for probe set, LLRLCR uses locality-constrained matrix regression to learn discriminative representation features robustly. The proposed method allows us to fully exploit the structure information in gallery and probe data simultaneously. Finally, after getting the resolution-robust features, a simple yet powerful sparse representation based classifier engine is used to predict the face labels. Experiments conducted on the AR database with occlusions have shown that the proposed method can obtain promising recognition performance than many state-of-the-art LR face recognition approaches.

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Metadata
Title
Low-Rank Representation and Locality-Constrained Regression for Robust Low-Resolution Face Recognition
Authors
Guangwei Gao
Pu Huang
Quan Zhou
Zangyi Hu
Dong Yue
Copyright Year
2018
Book
Artificial Intelligence and Robotics

Print ISBN: 978-3-319-69876-2

Electronic ISBN: 978-3-319-69877-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-69877-9_3

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