Regularized locality preserving discriminant embedding for face recognition

Ying Han Pang, Jin Teoh Andrew Beng, Fazly Salleh Abas

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


For face recognition, graph embedding techniques attempt to produce a high data locality projection for better recognition performance. However, estimation of population data locality could be severely biased due to small number of training samples. The biased estimation triggers overfitting problem and hence poor generalization. In this paper, we propose a new linear graph embedding technique based upon an adaptive locality preserving regulation model (ALPRM), known as Regularized Locality Preserving Discriminant Embedding (RLPDE). In RLPDE, the projection features are regulated based on ALPRM to approach population data locality, which can directly enhance the locality preserving capability of the projection features. This paper also presents the relation between locality preserving capability and class discrimination. Specifically, we show that the optimization of the locality preserving function minimizes the within-class variability. Experiments on three face datasets such as PIE, FRGC and FERET show the promising performance of the proposed technique.

Original languageEnglish
Pages (from-to)156-166
Number of pages11
Issue number1
Publication statusPublished - 2012 Feb 1

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


Dive into the research topics of 'Regularized locality preserving discriminant embedding for face recognition'. Together they form a unique fingerprint.

Cite this