DocumentCode :
178918
Title :
Human acoustic fingerprints: A novel biometric modality for mobile security
Author :
Yuxi Liu ; Hatzinakos, Dimitrios
Author_Institution :
Edward S. Roger Sr. Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada
fYear :
2014
fDate :
4-9 May 2014
Firstpage :
3784
Lastpage :
3788
Abstract :
Recently, the demand for more robust protection against unauthorized use of mobile devices has been rapidly growing. This paper presents a novel biometric modality Transient Evoked Otoacoustic Emission (TEOAE) for mobile security. Prior works have investigated TEOAE for biometrics in a setting where an individual is to be identified among a pre-enrolled identity gallery. However, this limits the applicability to mobile environment, where attacks in most cases are from imposters unknown to the system before. Therefore, we employ an unsupervised learning approach based on Autoencoder Neural Network to tackle such blind recognition problem. The learning model is trained upon a generic dataset and used to verify an individual in a random population. We also introduce the framework of mobile biometric system considering practical application. Experiments show the merits of the proposed method and system performance is further evaluated by cross-validation with an average EER 2.41% achieved.
Keywords :
acoustic signal processing; biometrics (access control); learning (artificial intelligence); mobile computing; mobile handsets; neural nets; otoacoustic emissions; autoencoder neural network; biometric modality; blind recognition problem; generic dataset; human acoustic fingerprints; learning model; mobile biometric system; mobile devices; mobile environment; mobile security; pre-enrolled identity gallery; transient evoked otoacoustic emission; unsupervised learning approach; Biometrics (access control); Feature extraction; Mobile communication; Neural networks; Security; Time-frequency analysis; Training; Autoencoder Neural Network; Biometric Verification; Mobile Security; Otoacoustic Emission; Time-frequency Analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on
Conference_Location :
Florence
Type :
conf
DOI :
10.1109/ICASSP.2014.6854309
Filename :
6854309
Link To Document :
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