The effect of time on ear biometrics
The effect of time on ear biometrics
We present an experimental study to demonstrate the effect of the time difference in image acquisition for gallery and probe on the performance of ear recognition. This experimental research is the first study on the time effect on ear biometrics. For the purpose of recognition, we convolve banana wavelets with an ear image and then apply local binary pattern on the convolved image. The histograms of the produced image are then used as features to describe an ear. A histogram intersection technique is then applied on the histograms of two ears to measure the ear similarity for the recognition purposes. We also use analysis of variance (ANOVA) to select features to identify the best banana wavelets for the recognition process. The experimental results show that the recognition rate is only slightly reduced by time. The average recognition rate of 98.5% is achieved for an eleven month-difference between gallery and probe on an un-occluded ear dataset of 1491 images of ears selected from Southampton University ear database.
Ibrahim, Mina Ibrahim
15dbd9a6-1960-4d53-abd3-58953b31ac97
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
11 October 2011
Ibrahim, Mina Ibrahim
15dbd9a6-1960-4d53-abd3-58953b31ac97
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Ibrahim, Mina Ibrahim, Nixon, Mark and Mahmoodi, Sasan
(2011)
The effect of time on ear biometrics.
In Proceedings of the International Joint Conference on Biometrics (IJCB), 2011.
IEEE..
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Abstract
We present an experimental study to demonstrate the effect of the time difference in image acquisition for gallery and probe on the performance of ear recognition. This experimental research is the first study on the time effect on ear biometrics. For the purpose of recognition, we convolve banana wavelets with an ear image and then apply local binary pattern on the convolved image. The histograms of the produced image are then used as features to describe an ear. A histogram intersection technique is then applied on the histograms of two ears to measure the ear similarity for the recognition purposes. We also use analysis of variance (ANOVA) to select features to identify the best banana wavelets for the recognition process. The experimental results show that the recognition rate is only slightly reduced by time. The average recognition rate of 98.5% is achieved for an eleven month-difference between gallery and probe on an un-occluded ear dataset of 1491 images of ears selected from Southampton University ear database.
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Published date: 11 October 2011
Additional Information:
Event Dates: 11-13 October 2011
Venue - Dates:
IEEE International Joint Conference on Biometrics (IJCB), 2011, Washington, United States, 2011-10-11 - 2011-10-13
Organisations:
Vision, Learning and Control
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Local EPrints ID: 272748
URI: http://eprints.soton.ac.uk/id/eprint/272748
PURE UUID: 28b8fde4-a330-440f-92b6-2393b9889951
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Date deposited: 06 Sep 2011 13:24
Last modified: 16 Mar 2024 02:34
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Author:
Mina Ibrahim Ibrahim
Author:
Sasan Mahmoodi
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