Adaptive algorithms applied to accelerometer biometrics in a data stream context

Adaptive algorithms applied to accelerometer biometrics in a data stream context

Author Pisani, Paulo Henrique Google Scholar
Autor UNIFESP Google Scholar
de Carvalho, Andre C. P. L. F. Google Scholar
Abstract The use of smartphone devices has increased over the last years, as illustrated by the growth in smartphone sales. These devices are currently used for several services, such as bank account access, social networks and storage of personal information. In view of this scenario, an important question arises: does authentication mechanisms already present in these devices provide enough security? Recently, a new authentication method, named accelerometer biometrics, has been proposed. This method allows the authentication of users using accelerometer data, which can be obtained from accelerometers usually present in modern smartphones. This is a clear advantage of this biometric modality, as there would be no additional cost with hardware. However, as a behavioral biometric technology, user models induced from accelerometer data may become outdated over time. This paper investigates the use of adaptation mechanisms to update user models in accelerometer biometrics in a data stream context. Practical issues regarding the usage of accelerometer data are also discussed.
Keywords Adaptive biometric systems
accelerometer biometrics
positive selection
biometric data streams
xmlui.dri2xhtml.METS-1.0.item-coverage Amsterdam
Language English
Sponsor Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Grant number FAPESP: 2012/25032-0
FAPESP: 2012/22608-8
FAPESP: 2013/07375-0
Date 2017
Published in Intelligent Data Analysis. Amsterdam, v. 21, n. 2, p. 353-370, 2017.
ISSN 1088-467X (Sherpa/Romeo, impact factor)
Publisher Ios Press
Extent 353-370
Access rights Closed access
Type Article
Web of Science ID WOS:000396260500008

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