3D Face Recognition Based on Local Shape Patterns and Sparse Representation Classifier

  • Di Huang
  • Karima Ouji
  • Mohsen Ardabilian
  • Yunhong Wang
  • Liming Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6523)

Abstract

In recent years, 3D face recognition has been considered as a major solution to deal with these unsolved issues of reliable 2D face recognition, i.e. illumination and pose variations. This paper focuses on two critical aspects of 3D face recognition: facial feature description and classifier design. To address the former one, a novel local descriptor, namely Local Shape Patterns (LSP), is proposed. Since LSP operator extracts both differential structure and orientation information, it can describe local shape attributes comprehensively. For the latter one, Sparse Representation Classifier (SRC) is applied to classify these 3D shape-based facial features. Recently, SRC has been attracting more and more attention of researchers for its powerful ability on 2D image-based face recognition. This paper continues to investigate its competency in shape-based face recognition. The proposed approach is evaluated on the IV2 3D face database containing rich facial expression variations, and promising experimental results are achieved which prove its effectiveness for 3D face recognition and insensitiveness to expression changes.

Keywords

3D face recognition local descriptor Local Shape Patterns (LSP) Sparse Representation Classifier (SRC) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Di Huang
    • 1
  • Karima Ouji
    • 1
  • Mohsen Ardabilian
    • 1
  • Yunhong Wang
    • 2
  • Liming Chen
    • 1
  1. 1.LIRIS LaboratoryCNRS 5205, Ecole Centrale LyonLyonFrance
  2. 2.School of Computer Science and EngineeringBeihang UniversityBeijingChina

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