Predicting sex as a soft-biometrics from device interaction swipe gestures

Miguel-Hurtado, Oscar and Stevenage, Sarah V. and Bevan, Chris and Guest, Richard (2016) Predicting sex as a soft-biometrics from device interaction swipe gestures. Pattern Recognition Letters, 79 . pp. 44-51. ISSN 0167-8655. (doi:https://doi.org/10.1016/j.patrec.2016.04.024) (Full text available)

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http://www.dx.doi.org/10.1016/j.patrec.2016.04.024

Abstract

Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices.

Item Type: Article
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Tina Thompson
Date Deposited: 26 May 2016 08:18 UTC
Last Modified: 28 Oct 2016 09:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55702 (The current URI for this page, for reference purposes)
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