Face recognition based on LBP and extreme learning machine

Jing Hui Wu, Lu Han, Jia Tong Li, Yi Xiao Zhao, Lin Bo Tang, Bao Jun Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This paper proposes a face recognition algorithm based on the combination of local binary pattern (LBP) texture features and extreme learning machine (ELM). The face image is divided into several regions, and the LBP features are extracted from these regions and combined together to form a feature vector which will be the input data of ELM. It shows that ELM performs well in classification applications, and ELM and support vector machine (SVM) are equivalent from the optimization point of view. But ELM has milder optimization constraints and much less training time. Our experiments are carried out on two well-known face databases, and the results show that compared with compared to PCA+NN, PCA+SVM and PCA+ELM the proposed method can achieve higher recognition rates.

Original languageEnglish
Title of host publicationVehicle, Mechatronics and Information Technologies
Pages3526-3529
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2013 - Zhengzhou, Henan, China
Duration: 17 Aug 201318 Aug 2013

Publication series

NameApplied Mechanics and Materials
Volume380-384
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Conference

Conference2013 International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2013
Country/TerritoryChina
CityZhengzhou, Henan
Period17/08/1318/08/13

Keywords

  • Extreme learning machine
  • Human face recognition
  • Local binary pattern
  • Support vector machine
  • Texture features

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