A distance weighted linear regression classifier based on optimized distance calculating approach for face recognition

Linlin Tang, Huifen Lu, Zhen Pang, Zhangyan Li, Jingyong Su

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Linear regression technique is an efficient method to solve face recognition problem. It’s based on the theory that images in the same class will also belong to same linear subspace and they can be represented through a linear equation. However, this method suffers from some misclassification problems for the infinite ductility of regression equation, moreover, it also doesn’t make a proper and full use of the information in each sample. For overcoming these problems, a novel algorithm named the Distance Weighted Regression Classifier (DWLRC) is proposed here. It can be used for face recognition under different expression and illumination conditions through a distance weighted method, and it can also be used for optimizing the error in the final distance calculating stage. Experiments on three benchmarks show the better performance of our DWLRC compared with the traditional LRC and some state-of-art methods.

Original languageEnglish
Pages (from-to)32485-32501
Number of pages17
JournalMultimedia Tools and Applications
Volume78
Issue number22
DOIs
StatePublished - Nov 1 2019

Keywords

  • Face recognition
  • Linear regression
  • Nearest subspace classifier
  • Object recognition

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