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Open AccessArticle

Smartphone User Identity Verification Using Gait Characteristics

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Department of Software Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
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Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
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Department of Computer Science, Kaunas University of Technology, 44249 Kaunas, Lithuania
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Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
*
Author to whom correspondence should be addressed.
Academic Editor: Young-Sik Jeong
Symmetry 2016, 8(10), 100; https://doi.org/10.3390/sym8100100
Received: 17 June 2016 / Revised: 4 September 2016 / Accepted: 21 September 2016 / Published: 29 September 2016
(This article belongs to the Special Issue Symmetry in Secure Cyber World)
Smartphone-based biometrics offers a wide range of possible solutions, which could be used to authenticate users and thus to provide an extra level of security and theft prevention. We propose a method for positive identification of smartphone user’s identity using user’s gait characteristics captured by embedded smartphone sensors (gyroscopes, accelerometers). The method is based on the application of the Random Projections method for feature dimensionality reduction to just two dimensions. Then, a probability distribution function (PDF) of derived features is calculated, which is compared against known user PDF. The Jaccard distance is used to evaluate distance between two distributions, and the decision is taken based on thresholding. The results for subject recognition are at an acceptable level: we have achieved a grand mean Equal Error Rate (ERR) for subject identification of 5.7% (using the USC-HAD dataset). Our findings represent a step towards improving the performance of gait-based user identity verification technologies. View Full-Text
Keywords: user identity verification; smartphone security; gait characteristics; Random Projections user identity verification; smartphone security; gait characteristics; Random Projections
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MDPI and ACS Style

Damaševičius, R.; Maskeliūnas, R.; Venčkauskas, A.; Woźniak, M. Smartphone User Identity Verification Using Gait Characteristics. Symmetry 2016, 8, 100.

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