Title

Bilateral Two-Dimensional Neighborhood Preserving Discriminant Embedding For Face Recognition

Keywords

bilateral two-dimensional neighborhood preserving embedding; Face recognition; Fisher's criterion; graph embedding; supervised learning

Abstract

In this paper, we propose a novel bilateral 2-D neighborhood preserving discriminant embedding for supervised linear dimensionality reduction for face recognition. It directly extracts discriminative face features from images based on graph embedding and Fisher's criterion. The proposed method is a manifold learning algorithm based on graph embedding criterion, which can effectively discover the underlying nonlinear face data structure. Both within-neighboring and between-neighboring information are taken into account to seek an optimal projection matrix by minimizing the intra-class scatter and maximizing the inter-class scatter based on Fisher's criterion. The performance of the proposed method is evaluated and compared with other face recognition schemes on the Yale, PICS, AR, and LFW databases. The experiment results demonstrate the effectiveness and superiority of the proposed method as compared with the state-of-the-art dimensionality reduction algorithms.

Publication Date

8-20-2017

Publication Title

IEEE Access

Volume

5

Number of Pages

17201-17212

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ACCESS.2017.2741223

Socpus ID

85028508322 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85028508322

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