DocumentCode :
1798176
Title :
An empirical analysis of ensemble systems in cancellable behavioural biometrics: A touch screen dataset
Author :
Damasceno, Marcelo ; Canuto, Anne M. P.
Author_Institution :
Dept. of Inf. & Appl. Math., Fed. Univ. of Rio Grande do Norte, Rio Grande, Brazil
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
2661
Lastpage :
2668
Abstract :
This paper presents an experimental analysis of a revocable biometric verification problem using ensemble systems. Behavioural Biometric-based systems are a future emergent area on identification, verification and access control systems of users. However, there is still progress to be done in this field, specially related to system security and acceptable results for practical use. Cancellable Biometrics is a alternative solution to the security problem of biometric data. This technique consists of applying transformation functions to biometric data in order to protect the original characteristics of biometric template. In this case, if biometric template has compromised, a new representation of original biometric data can be generated. Although cancellable biometrics were proposed to solve privacy concerns, this concept raises new issues, becoming the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. This work aims to investigate the use of ensemble systems in cancellable behavioural biometric system used by million people (touchscreen devices). Apart this, we also present an empirical analysis, comparing the ensemble structures with single classification algorithms.
Keywords :
biometrics (access control); pattern classification; touch sensitive screens; biometric template; cancellable behavioural biometrics; ensemble structures; ensemble systems; revocable biometric verification problem; single classification algorithms; touch screen dataset; Authentication; Bioinformatics; Biological system modeling; Iris recognition; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889819
Filename :
6889819
Link To Document :
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