Regularized locality preserving discriminant embedding for face recognition |
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Authors: | Ying Han PangAuthor Vitae Fazly Salleh AbasAuthor Vitae |
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Affiliation: | a Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia b Yonsei University, Seoul, South Korea c Predictive Intelligence Research Cluster, Sunway University, Bandar Sunway, 46150, P.J. Selangor, Malaysia |
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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. |
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Keywords: | Graph embedding Adaptive locality preserving regulation model Locality preserving capability Class discrimination Face recognition |
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