3D Morphable Model Construction for Robust Ear and Face Recognition
3D Morphable Model Construction for Robust Ear and Face Recognition
Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.
Bustard, John
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Nixon, Mark
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Bustard, John
acbc86fc-6914-4afb-a176-08e310d7b4f5
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Bustard, John and Nixon, Mark
(2010)
3D Morphable Model Construction for Robust Ear and Face Recognition.
IEEE Conf. Computer Vision and Patern Recognition CVPR 10, San Francisco.
(In Press)
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Conference or Workshop Item
(Other)
Abstract
Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.
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Accepted/In Press date: June 2010
Additional Information:
Event Dates: June 2010
Venue - Dates:
IEEE Conf. Computer Vision and Patern Recognition CVPR 10, San Francisco, 2010-06-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 270902
URI: http://eprints.soton.ac.uk/id/eprint/270902
PURE UUID: 1e3a982f-efec-43a0-b445-4d7525efbf39
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Date deposited: 22 Apr 2010 09:26
Last modified: 15 Mar 2024 02:35
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Author:
John Bustard
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