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Learning Neighborhood Discriminative Manifolds for Video-Based Face Recognition

  • John See
  • Mohammad Faizal Ahmad Fauzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

In this paper, we propose a new supervised Neighborhood Discriminative Manifold Projection (NDMP) method for feature extraction in video-based face recognition. The abundance of data in videos often result in highly nonlinear appearance manifolds. In order to extract good discriminative features, an optimal low-dimensional projection is learned from selected face exemplars by solving a constrained least-squares objective function based on both local neighborhood geometry and global manifold structure. The discriminative ability is enhanced through the use of intra-class and inter-class neighborhood information. Experimental results on standard video databases and comparisons with state-of-art methods demonstrate the capability of NDMP in achieving high recognition accuracy.

Keywords

Manifold learning feature extraction video-based face recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • John See
    • 1
  • Mohammad Faizal Ahmad Fauzi
    • 2
  1. 1.Faculty of Information TechnologyMultimedia University, Persiaran MultimediaCyberjayaMalaysia
  2. 2.Faculty of EngineeringMultimedia University, Persiaran MultimediaCyberjayaMalaysia

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