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Wavelet based approaches for detection and recognition in ear biometrics

Wavelet based approaches for detection and recognition in ear biometrics
Wavelet based approaches for detection and recognition in ear biometrics
One of the most recent trends in biometrics is recognition by ear appearance in head profile images. Ear localization to determine the region of interest containing ears is an important step in an ear biometric system. To this end, we propose a robust, simple and effective method for ear detection from profile images by employing a bank of curved and stretched Gabor wavelets, known as banana wavelets. Our analysis shows that the banana wavelets demonstrate better performance than Gabor wavelets technique for ear localization. This indicates that the curved wavelets are advantageous for the detection of curved structures such as ears. This ear detection technique is fully automated, has encouraging performance and appears to be robust to degradation by noise. Addition of a preprocessing stage, based on skin detection using colour and texture, can improve the detection results even further.

For recognition, we convolve the banana wavelets with an ear image and then apply local binary pattern (LBP) for texture analysis to the convolved image. The LBP histograms of the produced image are then used as features to describe an ear. A histogram intersection technique is then applied on the LBP histograms of two ears to measure their similarity for recognition. Analysis of variance is also exploited here to select features to identify the best banana filters for the recognition process. We show that the new banana wavelets, in combination with other analysis, can be used to achieve recognition by the ear, with practical advantages. The analyses focus particularly in simulating addition of noise and occlusion to a standard database, and their evaluation on a newer and much more demanding ear database.

We also present an experimental study to investigate the effect of time difference between image acquisition for gallery and probe on the performance of ear recognition. This experimental research is the first study on the effect of time on ear biometrics and show that the recognition rate remains unchanged over time, confirming another advantage of deploying the human ear as a biometric.
Ibrahim, Mina Ibrahim Samaan
097aa833-39d1-45f0-a556-868010099670
Ibrahim, Mina Ibrahim Samaan
097aa833-39d1-45f0-a556-868010099670
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf

(2012) Wavelet based approaches for detection and recognition in ear biometrics. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis, 152pp.

Record type: Thesis (Doctoral)

Abstract

One of the most recent trends in biometrics is recognition by ear appearance in head profile images. Ear localization to determine the region of interest containing ears is an important step in an ear biometric system. To this end, we propose a robust, simple and effective method for ear detection from profile images by employing a bank of curved and stretched Gabor wavelets, known as banana wavelets. Our analysis shows that the banana wavelets demonstrate better performance than Gabor wavelets technique for ear localization. This indicates that the curved wavelets are advantageous for the detection of curved structures such as ears. This ear detection technique is fully automated, has encouraging performance and appears to be robust to degradation by noise. Addition of a preprocessing stage, based on skin detection using colour and texture, can improve the detection results even further.

For recognition, we convolve the banana wavelets with an ear image and then apply local binary pattern (LBP) for texture analysis to the convolved image. The LBP histograms of the produced image are then used as features to describe an ear. A histogram intersection technique is then applied on the LBP histograms of two ears to measure their similarity for recognition. Analysis of variance is also exploited here to select features to identify the best banana filters for the recognition process. We show that the new banana wavelets, in combination with other analysis, can be used to achieve recognition by the ear, with practical advantages. The analyses focus particularly in simulating addition of noise and occlusion to a standard database, and their evaluation on a newer and much more demanding ear database.

We also present an experimental study to investigate the effect of time difference between image acquisition for gallery and probe on the performance of ear recognition. This experimental research is the first study on the effect of time on ear biometrics and show that the recognition rate remains unchanged over time, confirming another advantage of deploying the human ear as a biometric.

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More information

Published date: June 2012
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 340675
URI: http://eprints.soton.ac.uk/id/eprint/340675
PURE UUID: 07e255f7-2314-4772-95e8-a8bdd5131dec
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 29 Jun 2012 14:31
Last modified: 06 Jun 2018 13:18

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Contributors

Author: Mina Ibrahim Samaan Ibrahim
Thesis advisor: Mark Nixon ORCID iD
Thesis advisor: Sasan Mahmoodi

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