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
November 2012
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), Tsukuba, Japan.
11 - 15 Nov 2012.
.
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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Published date: November 2012
Venue - Dates:
21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 2012-11-11 - 2012-11-15
Organisations:
Vision, Learning and Control
Identifiers
Local EPrints ID: 363313
URI: http://eprints.soton.ac.uk/id/eprint/363313
ISBN: 978-1-4673-2216-4
PURE UUID: 7c7314a1-0a90-4907-888d-e24ffa4cb702
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Date deposited: 20 Mar 2014 16:45
Last modified: 15 Mar 2024 02:35
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Author:
Sung-Uk Jung
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