The University of Southampton
University of Southampton Institutional Repository

Robust Log-Gabor Filter for Ear Biometrics

Record type: Conference or Workshop Item (Poster)

Ears are a new biometric with major advantage in that they appear to maintain their structure with increasing age. Expanding on our previous parts-based model, we propose a new wavelet approach. In this, the log-Gabor filter exploits the frequency content of the ear boundary curves. Extending our model description, a specific aim of the new approach is to capture information in the ear’s outer structures. Ear biometrics is also concerned with the effects of partial occlusion, mostly by hair and earrings. By localization, intuitively a wavelet can offer performance advantage when handling occluded data. We also add a more robust matching strategy to restrict the influence of erroneous wavelet coefficients. Significant improvement is observed when we combine the model and the log- Gabor filter, and we will show that this improvement is maintained as the ears get occluded.

PDF Robust_Log-Gabor_Filter_for_Ear_Biometrics.pdf - Version of Record
Download (1MB)

Citation

Arbab-Zavar, Banafshe and Nixon, Mark (2008) Robust Log-Gabor Filter for Ear Biometrics At 19th International Conference on Pattern Recognition (ICPR 2008), United States. 08 - 11 Dec 2008.

More information

Published date: December 2008
Venue - Dates: 19th International Conference on Pattern Recognition (ICPR 2008), United States, 2008-12-08 - 2008-12-11
Related URLs:
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 266781
URI: http://eprints.soton.ac.uk/id/eprint/266781
PURE UUID: cda17345-8f76-4cce-a0e9-0e80fd307de9

Catalogue record

Date deposited: 13 Oct 2008 09:54
Last modified: 18 Jul 2017 07:12

Export record

Contributors

Author: Banafshe Arbab-Zavar
Author: Mark Nixon

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×