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On Model-Based Analysis of Ear Biometrics

On Model-Based Analysis of Ear Biometrics
On Model-Based Analysis of Ear Biometrics
Ears are a new biometric with major advantage in that they appear to maintain their structure with increasing age. Most current approaches are holistic and describe the ear by its general properties. We propose a new model-based approach, capitalizing on explicit structure and with the advantages of being robust in noise and occlusion. Our model is a constellation of generalized ear parts, which is learned off-line using an unsupervised learning algorithm over an enrolled training set of 63 ear images. The Scale Invariant Feature Transform (SIFT), is used to detect the features within the ear images. In recognition, given a profile image of the human head, the ear is enrolled and recognised from the parts selected via the model. We achieve an encouraging recognition rate, on an image database selected from the XM2VTS database. A head-to-head comparison with PCA is also presented to show the advantage derived by the use of the model in successful occlusion handling.
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Hurley, David
d0abd3e5-ffac-4160-bb00-042083251d79
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Hurley, David
d0abd3e5-ffac-4160-bb00-042083251d79

Arbab-Zavar, Banafshe, Nixon, Mark and Hurley, David (2007) On Model-Based Analysis of Ear Biometrics. First IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 07), United States. 27 - 29 Sep 2007.

Record type: Conference or Workshop Item (Other)

Abstract

Ears are a new biometric with major advantage in that they appear to maintain their structure with increasing age. Most current approaches are holistic and describe the ear by its general properties. We propose a new model-based approach, capitalizing on explicit structure and with the advantages of being robust in noise and occlusion. Our model is a constellation of generalized ear parts, which is learned off-line using an unsupervised learning algorithm over an enrolled training set of 63 ear images. The Scale Invariant Feature Transform (SIFT), is used to detect the features within the ear images. In recognition, given a profile image of the human head, the ear is enrolled and recognised from the parts selected via the model. We achieve an encouraging recognition rate, on an image database selected from the XM2VTS database. A head-to-head comparison with PCA is also presented to show the advantage derived by the use of the model in successful occlusion handling.

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More information

Published date: September 2007
Venue - Dates: First IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 07), United States, 2007-09-27 - 2007-09-29
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 264888
URI: https://eprints.soton.ac.uk/id/eprint/264888
PURE UUID: c8195c5e-8b58-4778-a884-98f10891c359
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 23 Nov 2007 12:42
Last modified: 06 Jun 2018 13:17

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