Paper Info
Title
Biometric identification based on Transient Evoked Otoacoustic Emission
Abstract
Biometrics provides a reliable and efficient solution to identity management in many aspects of daily lives, such as application login, access control and transaction security. This paper presents a novel approach to individual identification based on a new biometric modality Transient Evoked Otoacoustic Emission (TEOAE), which is a low level acoustic signal generated by human cochlea and detected in the outer ear canal. We resort to wavelet analysis to derive the time-frequency representation of such non-stationary signal and machine learning techniques: linear discriminant analysis and softmax regression to accomplish pattern recognition. We also introduce a complete framework of the biometric system considering practical application. Experiments on a TEOAE dataset of biometric setting show the merits of the proposed method. With fusion of information from both ears an average identification rate 98.72% is achieved.
Year
DOI
Venue
2013
10.1109/ISSPIT.2013.6781891
IEEE International Symposium on Signal Processing and Information Technology
Keywords
Field
DocType
Biometric Identification,Transient Evoked Otoacoustic Emission,Time-frequency Analysis,Softmax Regression,Pattern Recognition
Computer science,Artificial intelligence,Otoacoustic emission,Wavelet,Wavelet transform,Computer vision,Pattern recognition,Softmax function,Speech recognition,Feature extraction,Time–frequency analysis,Biometrics,Linear discriminant analysis
Conference
ISSN
Citations 
PageRank 
2162-7843
3
0.46
References 
Authors
4
2
Name
Order
Citations
PageRank
Yuxi Liu18613.46
Dimitrios Hatzinakos21200126.40