On Model-Based Analysis of Ear Biometrics


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

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Description/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.

Item Type: Conference or Workshop Item (Other)
Venue - Dates: First IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 07), United States, 2007-09-27 - 2007-09-29
Subjects:
Organisations: Southampton Wireless Group
ePrint ID: 264888
Date :
Date Event
September 2007Published
Date Deposited: 23 Nov 2007 12:42
Last Modified: 17 Apr 2017 19:29
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/264888

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