research-article

Local Structure-Based Sparse Representation for Face Recognition

Authors:

Zhenmin TangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 7, Issue 1

Article No.: 2, Pages 1 - 20

https://doi.org/10.1145/2733383

Published: 07 October 2015 Publication History

Abstract

This article presents a simple yet effective face recognition method, called local structure-based sparse representation classification (LS_SRC). Motivated by the “divide-and-conquer” strategy, we first divide the face into local blocks and classify each local block, then integrate all the classification results to make the final decision. To classify each local block, we further divide each block into several overlapped local patches and assume that these local patches lie in a linear subspace. This subspace assumption reflects the local structure relationship of the overlapped patches, making sparse representation-based classification (SRC) feasible even when encountering the single-sample-per-person (SSPP) problem. To lighten the computing burden of LS_SRC, we further propose the local structure-based collaborative representation classification (LS_CRC). Moreover, the performance of LS_SRC and LS_CRC can be further improved by using the confusion matrix of the classifier. Experimental results on four public face databases show that our methods not only generalize well to SSPP problem but also have strong robustness to occlusion; little pose variation; and the variations of expression, illumination, and time.

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Cited By

Liu FChen DWang FLi ZXu F(2023)Deep learning based single sample face recognition: a surveyArtificial Intelligence Review10.1007/s10462-022-10240-256:3(2723-2748)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10462-022-10240-2
Ding YTang ZWang F(2022)Single-Sample Face Recognition Based on Shared Generative Adversarial NetworkMathematics10.3390/math1005075210:5(752)Online publication date: 26-Feb-2022
https://doi.org/10.3390/math10050752
Dong Lyining yTongyu LYong ZYutong HShuai JBaoqiang M(2022)Dim and small targets based on the fusion of infrared radiation characteristics and spectral characteristics research on detection methodsEighth Symposium on Novel Photoelectronic Detection Technology and Applications10.1117/12.2625206(475)Online publication date: 27-Mar-2022
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Index Terms

Local Structure-Based Sparse Representation for Face Recognition
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 7, Issue 1

October 2015

293 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2830012

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2015 ACM.

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Association for Computing Machinery

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Publication History

Published: 07 October 2015

Accepted: 01 February 2015

Revised: 01 December 2014

Received: 01 August 2014

Published in TIST Volume 7, Issue 1

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Natural Science Foundation of Jiangsu Province
the Program for New Century Excellent Talents in University
Nature Science Foundation of China
the Research Fund for the Doctoral Program of Higher Education of China
973 Program

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Cited By

Liu FChen DWang FLi ZXu F(2023)Deep learning based single sample face recognition: a surveyArtificial Intelligence Review10.1007/s10462-022-10240-256:3(2723-2748)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10462-022-10240-2
Ding YTang ZWang F(2022)Single-Sample Face Recognition Based on Shared Generative Adversarial NetworkMathematics10.3390/math1005075210:5(752)Online publication date: 26-Feb-2022
https://doi.org/10.3390/math10050752
Dong Lyining yTongyu LYong ZYutong HShuai JBaoqiang M(2022)Dim and small targets based on the fusion of infrared radiation characteristics and spectral characteristics research on detection methodsEighth Symposium on Novel Photoelectronic Detection Technology and Applications10.1117/12.2625206(475)Online publication date: 27-Mar-2022
https://doi.org/10.1117/12.2625206
Pang MCheung YShi QLi M(2021)Iterative Dynamic Generic Learning for Face Recognition From a Contaminated Single-Sample Per PersonIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.298509932:4(1560-1574)Online publication date: Apr-2021
https://doi.org/10.1109/TNNLS.2020.2985099
Pang MWang BCheung YChen YWen B(2021)VD-GAN: A Unified Framework for Joint Prototype and Representation Learning From Contaminated Single Sample per PersonIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.305005516(2246-2259)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIFS.2021.3050055
Duan MOuyang ATan GTian Q(2021)Age Estimation Using Aging/Rejuvenation Features With Device-Edge SynergyIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.298111731:2(608-620)Online publication date: Feb-2021
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Duan MLi KLi KTian Q(2020)A Novel Multi-task Tensor Correlation Neural Network for Facial Attribute PredictionACM Transactions on Intelligent Systems and Technology10.1145/341828512:1(1-22)Online publication date: 13-Nov-2020
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Pang MCheung YWang BLou J(2020)Synergistic Generic Learning for Face Recognition From a Contaminated Single Sample per PersonIEEE Transactions on Information Forensics and Security10.1109/TIFS.2019.291995015(195-209)Online publication date: 2020
https://doi.org/10.1109/TIFS.2019.2919950
Ding YLiu FTang ZZhang T(2020)Uniform Generic Representation for Single Sample Face RecognitionIEEE Access10.1109/ACCESS.2020.30174798(158281-158292)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3017479
Liu FDing YXu FYe Q(2019)Learning low-rank regularized generic representation with block-sparse structure for single sample face recognitionIEEE Access10.1109/ACCESS.2019.2903333(1-1)Online publication date: 2019
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