TY - GEN
T1 - Adult or Child
T2 - 2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
AU - Rasheed, Osama
AU - Rextin, Aimal
AU - Nasim, Mehwish
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Children
KW - Human computer interaction
KW - Touch gestures
KW - User behavior and Machine learning
KW - User verification
UR - http://www.scopus.com/inward/record.url?scp=85078700501&partnerID=8YFLogxK
U2 - 10.1109/DICTA47822.2019.8945816
DO - 10.1109/DICTA47822.2019.8945816
M3 - Conference contribution
AN - SCOPUS:85078700501
T3 - 2019 Digital Image Computing: Techniques and Applications, DICTA 2019
BT - 2019 Digital Image Computing
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 December 2019 through 4 December 2019
ER -