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


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
Publication statusPublished - Oct 2019


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

Cite this