Discriminant sparse neighborhood preserving embedding for face recognition

Authors:
Jie Gui;Zhenan Sun;Wei Jia;Rongxiang Hu;Yingke Lei;Shuiwang Ji
Affiliations:
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China;Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China;Electronic Engineering Institute, Hefei Anhui 230037,China;Department of Computer Science, Old Dominion University, Norfolk, VA, USA, 23529-0162
Venue:
Pattern Recognition
Year:
2012

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Abstract

Sparse subspace learning has drawn more and more attentions recently. However, most of the sparse subspace learning methods are unsupervised and unsuitable for classification tasks. In this paper, a new sparse subspace learning algorithm called discriminant sparse neighborhood preserving embedding (DSNPE) is proposed by adding the discriminant information into sparse neighborhood preserving embedding (SNPE). DSNPE not only preserves the sparse reconstructive relationship of SNPE, but also sufficiently utilizes the global discriminant structures from the following two aspects: (1) maximum margin criterion (MMC) is added into the objective function of DSNPE; (2) only the training samples with the same label as the current sample are used to compute the sparse reconstructive relationship. Extensive experiments on three face image datasets (Yale, Extended Yale B and AR) demonstrate the effectiveness of the proposed DSNPE method.