N. Pears

Publications (3)0 Total impact

• Source

  Available from: york.ac.uk

  Conference Proceeding: Automatic Keypoint Detection on 3D Faces Using a Dictionary of Local Shapes

  C. Creusot, N. Pears, J. Austin
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  ABSTRACT: Keypoints on 3D surfaces are points that can be extracted repeatably over a wide range of 3D imaging conditions. They are used in many 3D shape processing applications, for example, to establish a set of initial correspondences across a pair of surfaces to be matched. Typically, keypoints are extracted using extremal values of a function over the 3D surface, such as the descriptor map for Gaussian curvature. That approach works well for salient points, such as the nose-tip, but can not be used with other less pronounced local shapes. In this paper, we present an automatic method to detect keypoints on 3D faces, where these keypoints are locally similar to a set of previously learnt shapes, constituting a 'local shape dictionary'. The local shapes are learnt at a set of 14 manually-placed landmark positions on the human face. Local shapes are characterised by a set of 10 shape descriptors computed over a range of scales. For each landmark, the proportion of face meshes that have an associated keypoint detection is used as a performance indicator. Repeatability of the extracted keypoints is measured across the FRGC v2 database.
  3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011 International Conference on; 06/2011
• Conference Proceeding: Three-dimensional face recognition: an eigensurface approach

  T. Heseltine, N. Pears, J. Austin
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  ABSTRACT: We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a range of image processing techniques we identify the most effective surface representation for use in such application areas as security, surveillance, data compression and archive searching.
  Image Processing, 2004. ICIP '04. 2004 International Conference on; 11/2004
• Source

  Available from: whiterose.ac.uk

  Article: Three-dimensional face recognition: An Eigensurface approach

  T. Heseltine, N. Pears, J. Austin
  [show abstract] [hide abstract]
  ABSTRACT: We evaluate a new approach to face recognition using a variety of surface representations of three-dimensional facial structure. Applying principal component analysis (PCA), we show that high levels of recognition accuracy can be achieved on a large database of 3D face models, captured under conditions that present typical difficulties to more conventional two-dimensional approaches. Applying a ran-c of image processing, techniques we identify the most effective surface representation for use in such application areas as security surveillance, data compression and archive searching.