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Multi-Eigenspace Learning for Video-Based Face Recognition

  • Liang Liu
  • Yunhong Wang
  • Tieniu Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

In this paper, we propose a novel online learning method called Multi-Eigenspace Learning which can learn appearance models incrementally from a given video stream. For each subject, we try to learn a few eigenspace models using IPCA (Incremental Principal Component Analysis). In the process of Multi-Eigenspace Learning, each eigenspace generally contains more and more samples except one eigenspace which contains the least number of samples. Then, these learnt eigenspace models are used for video-based face recognition. Experimental results show that the proposed method can achieve high recognition rate.

Keywords

face recognition online learning incremental principal component analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Liang Liu
    • 1
  • Yunhong Wang
    • 2
  • Tieniu Tan
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, BeijingChina
  2. 2.School of Computer Science and Engineering, Beihang University, BeijingChina

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