Regularized locality preserving discriminant embedding for face recognition


Pang, Ying Han and Teoh, Andrew Beng Jin and Abas, Fazly Salleh (2012) Regularized locality preserving discriminant embedding for face recognition. Neurocomputing, 77 (1). pp. 156-166. ISSN 0925-2312

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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. (C) 2011 Elsevier B.V. All rights reserved.

Item Type: Article
Uncontrolled Keywords: Graph embedding; Adaptive locality preserving regulation model; Locality preserving capability; Class discrimination; Face recognition
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 12 Mar 2012 09:59
Last Modified: 05 Jan 2017 04:15


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