Cascade MR-ASM for Locating Facial Feature Points

  • Sicong Zhang
  • Lifang Wu
  • Ying Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

Accurate and robust location of feature point is a difficult and challenging issue in face recognition. In this paper we propose a new approach of using a cascade of Multi-Resolution Active Shape Models (C-MR-ASM) to locate facial feature points. In our approach, more than one MR-ASMs are obtained from different subsets of training set automatically, and these MR-ASMs are integrated in a cascade to locate facial feature points. Experimental results show that our algorithm is more accurate than traditional MR-ASM. The contribution of this paper includes: 1, unlike traditional MR-ASM, the training set is divided into several subsets automatically based on the principle a trained model should describe all the samples in training set accurately. 2, we propose the new cascade framework, which integrates all the subset MR-ASM.

Keywords

Training Sample Feature Point Face Recognition Gray Level Face Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sicong Zhang
    • 1
  • Lifang Wu
    • 1
  • Ying Wang
    • 1
  1. 1.School of Electronic Information and control Engineering, Beijing University of Technology, Beijing, 100022China

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