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Model-based feature refinement by ellipsoidal face tracking

Model-based feature refinement by ellipsoidal face tracking
Model-based feature refinement by ellipsoidal face tracking
We describe a new method to relieve common assumptions/ restrictions in head tracking by using a model-based approach. This improves local feature matching which only considers the pattern around the extracted feature excluding the object shape, so that misalignment can occur. In this paper, to overcome constraints on motion we consider region- and distance-based feature refinement methods to validate the local features used when tracking the ellipsoidal object. We also present a direct mapping method to reconstruct 3D feature positions for tracking. The utility of the new method has been demonstrated for face pose estimation using the Boston face database.
978-1-4673-2216-4
1209-1212
Jung, Sung-Uk
8a00aee1-086e-48af-a9ab-7fdd066f5356
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Jung, Sung-Uk
8a00aee1-086e-48af-a9ab-7fdd066f5356
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Jung, Sung-Uk and Nixon, Mark S. (2012) Model-based feature refinement by ellipsoidal face tracking. 21st International Conference on Pattern Recognition (ICPR 2012), Japan. 11 - 15 Nov 2012. pp. 1209-1212 .

Record type: Conference or Workshop Item (Paper)

Abstract

We describe a new method to relieve common assumptions/ restrictions in head tracking by using a model-based approach. This improves local feature matching which only considers the pattern around the extracted feature excluding the object shape, so that misalignment can occur. In this paper, to overcome constraints on motion we consider region- and distance-based feature refinement methods to validate the local features used when tracking the ellipsoidal object. We also present a direct mapping method to reconstruct 3D feature positions for tracking. The utility of the new method has been demonstrated for face pose estimation using the Boston face database.

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More information

Published date: November 2012
Venue - Dates: 21st International Conference on Pattern Recognition (ICPR 2012), Japan, 2012-11-11 - 2012-11-15
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 363313
URI: https://eprints.soton.ac.uk/id/eprint/363313
ISBN: 978-1-4673-2216-4
PURE UUID: 7c7314a1-0a90-4907-888d-e24ffa4cb702
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 20 Mar 2014 16:45
Last modified: 20 Jul 2019 01:28

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