The University of Southampton
University of Southampton Institutional Repository

On distinctiveness and symmetry in ear biometrics

On distinctiveness and symmetry in ear biometrics
On distinctiveness and symmetry in ear biometrics
Previous works show that human ears can be used for identification, gender classification, and kinship verification and have investigated whether there is a symmetry between a person’s ears; however, the symmetry performances have been less than satisfactory. Our paper extends the analysis of gender classification on ear images and analyses bilateral symmetry of human ears, in both cases aiming to determine the ear parts from which recognition is derived. We use model-based analysis and deep learning methods to capitalize on structure and performance, respectively. We consider the rotation of ear images under an affine transformation, by modelling the ear as a flat plane attached to the head. We address the question as to whether it is possible that given an image of one ear, a person can then be recognized from their other ear. Such a symmetry based strategy could reduce constraints on applications of ear biometrics. We show that it is possible to recognise the gender with a 90.9% success rate and that the ear rim (the upper helix and lobe) dominates performance. To investigate symmetry, we compare one ear with a mirrored version of the other ear and achieve 93.1% CCR, which is the current state-of-the-art, with important regions different from those determined for gender. To extend the analysis we construct two groups of images, one of which contains both ears from the same subject and the other contains two ears from different subjects. The 100% CCR confirms the existence of symmetry between a subject’s ears. By these approaches we show that there is actually a high chance that there exists symmetry between a person’s ears and that it would be prudent for recognition systems to concentrate on the inner ear rather than the outer ear.
gender classification, ear symmetry, deep learning, heatmaps, model-based
2637-6407
Meng, Di
ec8d62a6-c99c-4fbf-93e3-ff705c6a8279
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Meng, Di
ec8d62a6-c99c-4fbf-93e3-ff705c6a8279
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf

Meng, Di, Nixon, Mark and Mahmoodi, Sasan (2021) On distinctiveness and symmetry in ear biometrics. IEEE Transactions on Biometrics, Behavior, and Identity Science. (doi:10.1109/TBIOM.2021.3058562).

Record type: Article

Abstract

Previous works show that human ears can be used for identification, gender classification, and kinship verification and have investigated whether there is a symmetry between a person’s ears; however, the symmetry performances have been less than satisfactory. Our paper extends the analysis of gender classification on ear images and analyses bilateral symmetry of human ears, in both cases aiming to determine the ear parts from which recognition is derived. We use model-based analysis and deep learning methods to capitalize on structure and performance, respectively. We consider the rotation of ear images under an affine transformation, by modelling the ear as a flat plane attached to the head. We address the question as to whether it is possible that given an image of one ear, a person can then be recognized from their other ear. Such a symmetry based strategy could reduce constraints on applications of ear biometrics. We show that it is possible to recognise the gender with a 90.9% success rate and that the ear rim (the upper helix and lobe) dominates performance. To investigate symmetry, we compare one ear with a mirrored version of the other ear and achieve 93.1% CCR, which is the current state-of-the-art, with important regions different from those determined for gender. To extend the analysis we construct two groups of images, one of which contains both ears from the same subject and the other contains two ears from different subjects. The 100% CCR confirms the existence of symmetry between a subject’s ears. By these approaches we show that there is actually a high chance that there exists symmetry between a person’s ears and that it would be prudent for recognition systems to concentrate on the inner ear rather than the outer ear.

Text
09353853 - Accepted Manuscript
Download (1MB)
Text
TBIOMPaper - Version of Record
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 2 February 2021
e-pub ahead of print date: 12 February 2021
Keywords: gender classification, ear symmetry, deep learning, heatmaps, model-based

Identifiers

Local EPrints ID: 446989
URI: http://eprints.soton.ac.uk/id/eprint/446989
ISSN: 2637-6407
PURE UUID: 82d53e45-41cb-4045-861d-9ef4ff5afe64
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 01 Mar 2021 17:32
Last modified: 02 Mar 2021 02:32

Export record

Altmetrics

Contributors

Author: Di Meng
Author: Mark Nixon ORCID iD
Author: Sasan Mahmoodi

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.

×