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.