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), Washington, US, 27 - 29 Sep 2007.
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 (Speech)|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QP Physiology
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||23 Nov 2007 12:42|
|Last Modified:||27 Mar 2014 20:09|
|Further Information:||Google Scholar|
|ISI Citation Count:||0|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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