Representation-based classification methods with enhanced linear reconstruction measures for face recognition

Jianping Gou, Jun Song, Weihua Ou, Shaoning Zeng, Yunhao Yuan, Lan Du

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.
Original languageEnglish
Article number106451
Number of pages15
JournalComputers and Electrical Engineering
Volume79
DOIs
Publication statusPublished - Oct 2019

Keywords

  • Collaborative representation
  • Sparsity augmented collaborative
  • representation
  • Representation-based classification
  • Linear reconstruction measurement

Cite this

@article{cb09678dab0540a7a0385e0099970b6e,
title = "Representation-based classification methods with enhanced linear reconstruction measures for face recognition",
abstract = "Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.",
keywords = "Collaborative representation, Sparsity augmented collaborative, representation, Representation-based classification, Linear reconstruction measurement",
author = "Jianping Gou and Jun Song and Weihua Ou and Shaoning Zeng and Yunhao Yuan and Lan Du",
year = "2019",
month = "10",
doi = "10.1016/j.compeleceng.2019.106451",
language = "English",
volume = "79",
journal = "Computers and Electrical Engineering",
issn = "0045-7906",
publisher = "Elsevier",

}

Representation-based classification methods with enhanced linear reconstruction measures for face recognition. / Gou, Jianping ; Song, Jun; Ou, Weihua; Zeng, Shaoning; Yuan, Yunhao; Du, Lan.

In: Computers and Electrical Engineering, Vol. 79, 106451, 10.2019.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Representation-based classification methods with enhanced linear reconstruction measures for face recognition

AU - Gou, Jianping

AU - Song, Jun

AU - Ou, Weihua

AU - Zeng, Shaoning

AU - Yuan, Yunhao

AU - Du, Lan

PY - 2019/10

Y1 - 2019/10

N2 - Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.

AB - Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.

KW - Collaborative representation

KW - Sparsity augmented collaborative

KW - representation

KW - Representation-based classification

KW - Linear reconstruction measurement

U2 - 10.1016/j.compeleceng.2019.106451

DO - 10.1016/j.compeleceng.2019.106451

M3 - Article

VL - 79

JO - Computers and Electrical Engineering

JF - Computers and Electrical Engineering

SN - 0045-7906

M1 - 106451

ER -