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2018 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Artificial Intelligence and Robotics

Verlag: 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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Titel: Low-Rank Representation and Locality-Constrained Regression for Robust Low-Resolution Face Recognition
verfasst von: Guangwei Gao
Pu Huang
Quan Zhou
Zangyi Hu
Dong Yue
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Robotics
Print ISBN: 978-3-319-69876-2

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

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-69877-9_3

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