Face recognition using LBP Eigenfaces

Lei Lei, Dae Hwan Kim, Won Jae Park, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In this paper, we propose a simple and efficient face representation feature that adopts the eigenfaces of Local Binary Pattern (LBP) space, referred to as the LBP eigenfaces, for robust face recognition. In the proposed method, LBP eigenfaces are generated by first mapping the original image space to the LBP space and then projecting the LBP space to the LBP eigenface subspace by Principal Component Analysis (PCA). Therefore, LBP eigenfaces capture both the local and global structures of face images. In the experiments, the proposed LBP eigenfaces are integrated into two types of classification methods, Nearest Neighbor (NN) and Collaborative Representation-based Classification (CRC). Experimental results indicate that the classification with the LBP eigenfaces outperforms that with the original eigenfaces and LBP histogram.

Original languageEnglish
Pages (from-to)1930-1932
Number of pages3
JournalIEICE Transactions on Information and Systems
Issue number7
Publication statusPublished - 2014 Jul


  • Eigenfaces
  • Face recognition
  • LBP
  • PCA

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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