Locality Regularization Embedding for face verification

Ying Han Pang, Andrew Beng Jin Teoh, Fu San Hiew

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Graph embedding (GE) is a unified framework for dimensionality reduction techniques. GE attempts to maximally preserve data locality after embedding for face representation and classification. However, estimation of true data locality could be severely biased due to limited number of training samples, which trigger overfitting problem. In this paper, a graph embedding regularization technique is proposed to remedy this problem. The regularization model, dubbed as Locality Regularization Embedding (LRE), adopts local Laplacian matrix to restore true data locality. Based on LRE model, three dimensionality reduction techniques are proposed. Experimental results on five public benchmark face datasets such as CMU PIE, FERET, ORL, Yale and FRGC, along with Nemenyi Post-hoc statistical of significant test attest the promising performance of the proposed techniques.

Original languageEnglish
Pages (from-to)86-102
Number of pages17
JournalPattern Recognition
Issue number1
Publication statusPublished - 2015 Jan 1

Bibliographical note

Funding Information:
This research was supported by UM-MMU Collaboration and Fundamental Research Grant Scheme – FRGS (#MMUE/140020) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2013006574).

Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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