ChildShield: An Implicit and Continuous Child Identification System on Smartphones

Chenhao Lin, Tianle Song, Yingmao Miao, Jingyi He, Minghui Yang, Huan Wang, Chao Shen

Abstract: Smartphone addiction among child users is becoming a severe global social problem. Uncontrolled and unsupervised use of smartphones by children has posed a significant threat to the health and property of both children and their parents. Automatic identification of child users on smartphones can be an effective way to alleviate this problem. Unfortunately, existing works usually require additional input devices like cameras for face biometrics or additional applications for users’ specific touch-interaction behavior to identify child users, leading to poor user experience and privacy concerns. This paper develops a novel, implicit and continuous system, named ChildShield, for child identification on smartphones. Specifically, our system providing a built-in data acquisition service can automatically and real-timely collect users’ behavioral data in a non-conscious and privacy-preserving manner. We build a large-scale database by collecting users’ operations in 5 complex and popular mobile game applications on 12 different models of smartphones from 1875 subjects. Based on the feature extracted from multi-finger interaction data in realistic and complex usage scenarios, ChildShield can learn the discriminative behavioral patterns for accurate child identification using the specifically designed deep learning-based classifiers. Then when a child user is identified, the pre-setting subsequent operation like Enable Kids Mode in ChildShield can be executed to provide a protective shield for children. The effectiveness of ChildShield is validated on the created database. Our approach significantly outperforms existing methods, achieves an EER of 4.38% for child identification, and performs an even lower EER of 2.12% for the younger age group.