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From 3D Point Clouds to Pose-Normalised Depth Maps

Pears, Nick (orcid.org/0000-0001-9513-5634), Heseltine, Tom and Romero, Marcelo (2010) From 3D Point Clouds to Pose-Normalised Depth Maps. International Journal of Computer Vision. pp. 152-176. ISSN 0920-5691

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We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data).

Item Type: Article
Copyright, Publisher and Additional Information: © 2010 Springer Verlag. This is an author produced version of a paper published in INTERNATIONAL JOURNAL OF COMPUTER VISION. Uploaded in accordance with the publisher's self archiving policy.
Keywords: 3D feature extraction,Invariance,3D landmark localisation,3D pose normalisation,3-DIMENSIONAL FACE RECOGNITION,OBJECT RECOGNITION,REPRESENTATION,IMAGES,INTERPOLATION,EIGENFACES,EXPRESSION,PROJECTION,ALGORITHM,FEATURES,Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Institution: The University of York
Academic Units: The University of York > Computer Science (York)
Depositing User: Repository Administrator York
Date Deposited: 20 Jun 2010 18:15
Last Modified: 17 Jul 2016 00:01
Published Version: http://dx.doi.org/10.1007/s11263-009-0297-y
Status: Published
Refereed: Yes
Related URLs:
URI: http://eprints.whiterose.ac.uk/id/eprint/10928

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