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Classification of acceleration data for biometric gait recognition on mobile devices

dc.contributor.authorNickel, Claudia
dc.contributor.authorBrandt, Holger
dc.contributor.authorBusch, Christoph
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.date.accessioned2018-11-27T09:53:31Z
dc.date.available2018-11-27T09:53:31Z
dc.date.issued2011
dc.description.abstractUbiquitous mobile devices like smartphones and tablets are often not secured against unauthorized access as the users tend to not use passwords because of convenience reasons. Therefore, this study proposes an alternative user authentication method for mobile devices based on gait biometrics. The gait characteristics are captured using the built-in accelerometer of a smartphone. Various features are extracted from the measured accelerations and utilized to train a support vector machine (SVM). Among the extracted features are the Meland Bark-frequency cepstral coefficients (MFCC, BFCC) which are commonly used in speech and speaker recognition and have not been used for gait recognition previously. The proposed approach showed competitive recognition performance, yielding 5.9% FMR at 6.3% FNMR in a mixedday scenario.en
dc.identifier.isbn978-3-88579-285-7
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/18563
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2011 – Proceedings of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-191
dc.titleClassification of acceleration data for biometric gait recognition on mobile devicesen
dc.typeText/Conference Paper
gi.citation.endPage66
gi.citation.publisherPlaceBonn
gi.citation.startPage57
gi.conference.date08.-09. September 2011
gi.conference.locationDarmstadt
gi.conference.sessiontitleRegular Research Papers

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