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The biometric potential of transient otoacoustic emissions

The biometric potential of transient otoacoustic emissions Whilst the hearing capabilities of the ear are well known and extensively studied, less well known is the fact that the ear can produce sounds. These faint sounds are called otoacoustic emissions and are an involuntary feature of the biomechanical system employed to hear low amplitude sounds. Several distinct types of emission are known; of these, one particular type, transient otoacoustic emissions (TEOAEs), shows potential as a biometric. This paper graphically presents examples of TEOAEs to demonstrate the specificity of TEOAEs to an individual and their stability over a six month period of time. Several large datasets (760 and 561 subjects) and a smaller dataset are numerically analysed to classify individuals and quantify permanence over six months. It was discovered that a high level of classification performance can be obtained using the raw time-pressure data without transformation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

The biometric potential of transient otoacoustic emissions

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References (5)

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2009.024278
Publisher site
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Abstract

Whilst the hearing capabilities of the ear are well known and extensively studied, less well known is the fact that the ear can produce sounds. These faint sounds are called otoacoustic emissions and are an involuntary feature of the biomechanical system employed to hear low amplitude sounds. Several distinct types of emission are known; of these, one particular type, transient otoacoustic emissions (TEOAEs), shows potential as a biometric. This paper graphically presents examples of TEOAEs to demonstrate the specificity of TEOAEs to an individual and their stability over a six month period of time. Several large datasets (760 and 561 subjects) and a smaller dataset are numerically analysed to classify individuals and quantify permanence over six months. It was discovered that a high level of classification performance can be obtained using the raw time-pressure data without transformation.

Journal

International Journal of Biometrics – Inderscience Publishers

Published: Jan 1, 2009

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