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3-D Face Recognition Using Geodesic-Map Representation and Statistical Shape Modelling

  • Wei QuanEmail author
  • Bogdan J. Matuszewski
  • Lik-Kwan Shark
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9493)

Abstract

3-D face recognition research has received significant attention in the past two decades because of the rapid development in imaging technology and ever increasing security demand of modern society. One of its challenges is to cope with non-rigid deformation among faces, which is often caused by the changes of appearance and facial expression. Popular solutions to deal with this problem are to detect the deformable parts of the face and exclude them, or to represent a face in terms of sparse signature points, curves or patterns that are invariant to deformation. Such approaches, however, may lead to loss of information which is important for classification. In this paper, we propose a new geodesic-map representation with statistical shape modelling for handling the non-rigid deformation challenge in face recognition. The proposed representation captures all geometrical information from the entire 3-D face and provides a compact and expression-free map that preserves intrinsic geometrical information. As a result, the search for dense points correspondence in the face recognition task can be speeded up by using a simple image-based method instead of time-consuming, recursive closest distance search in 3-D space. An experimental investigation was conducted on 3-D face scans using publicly available databases and compared with the benchmark approaches. The experimental results demonstrate that the proposed scheme provides a highly competitive new solution for 3-D face recognition.

Keywords

3-D face recognition Non-rigid deformation Shape modelling Geodesic-map representation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wei Quan
    • 1
    Email author
  • Bogdan J. Matuszewski
    • 1
  • Lik-Kwan Shark
    • 1
  1. 1.Applied Digital Signal and Image Processing (ADSIP) Research CentreUniversity of Central LancashirePrestonUK

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