Gait Recognition as an Authentication Method for Mobile Devices
With the rate at which smartphones are currently evolving, more and more of human life will be contained in these devices. At a time when data privacy is extremely important, it is crucial to protect one’s mobile device. In this paper, we propose a new non-intrusive gait recognition based mechanism...
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doaj-7f51f7ce91db45fea997fcc24c661eec2020-11-25T03:39:20ZengMDPI AGSensors1424-82202020-07-01204110411010.3390/s20154110Gait Recognition as an Authentication Method for Mobile DevicesMatei-Sorin Axente0Ciprian Dobre1Radu-Ioan Ciobanu2Raluca Purnichescu-Purtan3Faculty of Automatic Control and Computers, University Politehnica of Bucharest, RO-060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University Politehnica of Bucharest, RO-060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, University Politehnica of Bucharest, RO-060042 Bucharest, RomaniaDepartment of Mathematical Methods and Models, University Politehnica of Bucharest, RO-060042 Bucharest, RomaniaWith the rate at which smartphones are currently evolving, more and more of human life will be contained in these devices. At a time when data privacy is extremely important, it is crucial to protect one’s mobile device. In this paper, we propose a new non-intrusive gait recognition based mechanism that can enhance the security of smartphones by rapidly identifying users with a high degree of confidence and securing sensitive data in case of an attack, with a focus on a potential architecture for such an algorithm for the Android environment. The motion sensors on an Android device are used to create a statistical model of a user’s gait, which is later used for identification. Through experimental testing, we prove the capability of our proposed solution by correctly classifying individuals with an accuracy upwards of 90% when tested on data recorded during multiple activities. The experiments, conducted on a low sampling rate and at short time intervals, show the benefits of our solution and highlight the feasibility of an efficient gait recognition mechanism on modern smartphones.https://www.mdpi.com/1424-8220/20/15/4110mobile devicessmartphonesauthenticationprivacygait detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Matei-Sorin Axente Ciprian Dobre Radu-Ioan Ciobanu Raluca Purnichescu-Purtan |
spellingShingle |
Matei-Sorin Axente Ciprian Dobre Radu-Ioan Ciobanu Raluca Purnichescu-Purtan Gait Recognition as an Authentication Method for Mobile Devices Sensors mobile devices smartphones authentication privacy gait detection |
author_facet |
Matei-Sorin Axente Ciprian Dobre Radu-Ioan Ciobanu Raluca Purnichescu-Purtan |
author_sort |
Matei-Sorin Axente |
title |
Gait Recognition as an Authentication Method for Mobile Devices |
title_short |
Gait Recognition as an Authentication Method for Mobile Devices |
title_full |
Gait Recognition as an Authentication Method for Mobile Devices |
title_fullStr |
Gait Recognition as an Authentication Method for Mobile Devices |
title_full_unstemmed |
Gait Recognition as an Authentication Method for Mobile Devices |
title_sort |
gait recognition as an authentication method for mobile devices |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-07-01 |
description |
With the rate at which smartphones are currently evolving, more and more of human life will be contained in these devices. At a time when data privacy is extremely important, it is crucial to protect one’s mobile device. In this paper, we propose a new non-intrusive gait recognition based mechanism that can enhance the security of smartphones by rapidly identifying users with a high degree of confidence and securing sensitive data in case of an attack, with a focus on a potential architecture for such an algorithm for the Android environment. The motion sensors on an Android device are used to create a statistical model of a user’s gait, which is later used for identification. Through experimental testing, we prove the capability of our proposed solution by correctly classifying individuals with an accuracy upwards of 90% when tested on data recorded during multiple activities. The experiments, conducted on a low sampling rate and at short time intervals, show the benefits of our solution and highlight the feasibility of an efficient gait recognition mechanism on modern smartphones. |
topic |
mobile devices smartphones authentication privacy gait detection |
url |
https://www.mdpi.com/1424-8220/20/15/4110 |
work_keys_str_mv |
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