Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/126647
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Type: Conference paper
Title: Adult or child: Recognizing through touch gestures on smartphones
Author: Rasheed, O.
Rextin, A.
Nasim, M.
Citation: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2019), 2019 / pp.1-8
Publisher: IEEE
Publisher Place: online
Issue Date: 2019
ISBN: 9781728138572
Conference Name: International Conference on Digital Image Computing: Technqiues and Applications (DICTA) (02 Dec 2019 - 04 Dec 2019 : Perth, Australia)
Statement of
Responsibility: 
Osama Rasheed, Aimal Rextin, Mehwish Nasim
Abstract: With the increasing popularity of smartphones and its audience including children as young as 2 year old, smartphones can be a hazard for young children in terms of health concerns, time wastage, viewing of inappropriate material and conversely children who are too young can be a threat to the smartphone as well e.g, causing battery drainage, making unwanted calls/text messages, doing physical damage etc. In order to protect the smartphone and children from each other, we require user identification on our devices so the device could perform certain functions for instance restricting adult content once a user is identified as a child. This paper is a user study that aims at detecting the touch patterns of adults and children. To this end we collected data from 60 people, 30 adults and 30 children while they were asked to perform the 6 basic tasks that are performed on touch devices to find the differences in the touch gestures of children from adults. We first perform an exploratory data analysis. We then model the problem as a supervised binary classification problem and use the data as input for different machine learning algorithms to find whether we can classify a user previously unknown to the machine as an adult or a child. Our work shows there are differences in touch gestures among children and adults which are sufficient for user group identification.
Keywords: Touch gestures; User verification; Children; Human computer interaction; User behavior and Machine learning
Rights: © 2019 IEEE
RMID: 1000014113
DOI: 10.1109/DICTA47822.2019.8945816
Published version: https://ieeexplore.ieee.org/xpl/conhome/8943071/proceeding
Appears in Collections:Mathematical Sciences publications

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