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A Case Study Using Zoom Touch Gestures: How Does the Size of a Training Dataset Impact User’s Age Estimation Accuracy in Smartphones?
Resource type
Author/contributor
- Hossain, Md Shafaeat (Author)
Title
A Case Study Using Zoom Touch Gestures: How Does the Size of a Training Dataset Impact User’s Age Estimation Accuracy in Smartphones?
Abstract
In this paper, we focus on improving the age estimation accuracy on smartphones. Estimating a smartphone user’s age has several applications such as protecting our children online by filtering age-inappropriate contents, providing a customized e-commerce experience, etc. However, accuracy of the the state-of-the-art age estimation techniques that use touch behavior on smartphones is still limited because of the lack of sufficient amount of training data. We perform rigorous experiments using zoom gestures on smartphones and demonstrate that increasing the amount of training data can significantly improve the age estimation accuracy. Based on the findings in this study, we recommend creating a large touch dynamics-based age estimation data set so that more accurate age estimation models can be built and in turn, can be used more confidently.
Proceedings Title
2023 IEEE International Conference on Smart Computing (SMARTCOMP)
Conference Name
2023 IEEE International Conference on Smart Computing (SMARTCOMP)
Date
2023-06
Pages
192-194
Citation Key
hossainCaseStudyUsing2023
ISSN
2693-8340
Short Title
A Case Study Using Zoom Touch Gestures
Library Catalog
IEEE Xplore
Extra
0 citations (Crossref) [2023-10-31]
Citation
Hossain, M. S. (2023). A Case Study Using Zoom Touch Gestures: How Does the Size of a Training Dataset Impact User’s Age Estimation Accuracy in Smartphones? 2023 IEEE International Conference on Smart Computing (SMARTCOMP), 192–194. https://doi.org/10.1109/SMARTCOMP58114.2023.00044
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