Regularized locality preserving discriminant embedding for face recognition

Authors:
Ying Han Pang;Jin Teoh Andrew Beng;Fazly Salleh Abas
Affiliations:
Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;Yonsei University, Seoul, South Korea and Predictive Intelligence Research Cluster, Sunway University, Bandar Sunway, 46150, P.J. Selangor, Malaysia;Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia
Venue:
Neurocomputing
Year:
2012

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Abstract

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.