Gait fingerprinting-based user identification on smartphones

Document Type

Conference Proceeding

Source of Publication

Proceedings of the International Joint Conference on Neural Networks

Publication Date

10-31-2016

Abstract

© 2016 IEEE. Smartphones have ubiquitously integrated into our home and work environments. It is now a common practice for people to store their sensitive and confidential information on their phones. This has made it extremely important to authenticate legitimate users of a phone and block imposters. In this paper, we demonstrate that the motion dynamics of smartphones, captured using their built in accelerometers, can be used for accurate user identification. We call this mechanism gait fingerprinting. To this end, we first collected the acceleration data from multiple users as they walked with a smartphone placed freely in their pants pockets. Next, we studied the application of different feature extraction, feature selection and classification techniques from the machine learning literature on these data. Through extensive experimentation, demonstrated is that simple time domain features extracted from these data, which are further optimized using stepwise linear discrimination analysis, can be used to train artificial neural networks to identify legitimate user and block imposter with an average accuracy of 95%.

ISBN

9781509006199

Publisher

Institute of Electrical and Electronics Engineers Inc.

Volume

2016-October

First Page

3060

Last Page

3067

Disciplines

Computer Sciences

Keywords

Imposter, Smartphone, Ubiquitous, User identification

Scopus ID

85007236019

Indexed in Scopus

yes

Open Access

no

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