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Identifying Child Users via Touchscreen Interactions

delete2020-07-28
delete19
PRE
AI
Y
Yushi Cheng
X
Xiaoyu Ji
X
Xiaopeng Li
T
Tianchen Zhang
S
Sharaf J. Malebary
X
Xianshan Qu
W
Wenyuan Xu *
DOI:10.1145/3403574delete
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摘要

摘要

En 中文
With the proliferation of smart devices, children can be easily exposed to violent or adult-only content on the Internet. Without any precaution, the premature and unsupervised use of smart devices can be harmful to both children and their parents. Thus, it is critical to employ parent patrol mechanisms such that children are restricted to child-friendly content only. A successful parent patrol strategy has to be user friendly and privacy aware. The apps that require explicit actions from parents are not effective because a parent may forget to enable them, and the ones that use built-in cameras or microphones to detect child users may impose privacy violations. In this article, we propose iCare, a system that can identify child users automatically and seamlessly when users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users from the aspect of physiological maturity. We discover that one's touch behaviors are related to his or her age. Thus, iCare records the touch behaviors and extracts hand geometry, finger dexterity, and hand stability features that capture the age information. We conduct experiments on 100 people including 62 children (3 to 17 years old) and 38 adults (18 to 59 years old). Results show that iCare can achieve 96.6% accuracy for child identification using only a single swipe on the screen, and the accuracy becomes 98.3% with three consecutive swipes.
Keyword:
Child identification
touch behavior
automatic
implicit
AI总结

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期刊

ACM Transactions on Sensor Networks 封面图
ACM Transactions on Sensor Networks
IF:
4.7
论文数:
999
被引数:
2.0K

机构

U
university of south carolina columbia
学者数:
9.6K
论文数: 8.5K
被引数: 7
Z
zhejiang university
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17.7W
论文数: 12.1W
被引数: 152
U
University of South Carolina System
学者数:
1.6W
论文数: 1.5W
被引数: 27
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