The effectiveness of zoom touchscreen gestures for authentication and identification and its changes over time

• Published in: Computers & security Vol. 111; p. 102462
• Main Authors: Wang, Leran, Hossain, Md Shafaeat, Pulfrey, Joshua, Lancor, Lisa
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.12.2021; Elsevier Sequoia S.A
• Subjects: Authentication; Behavioral Biometrics; Classifiers; Data collection; Feature extraction; Identification; Image classification; Interactive computer systems; Smartphone; Smartphones; Support vector machines; Touch Gesture; Touch screens; Zoom; Zoom; Behavioral Biometrics; Touch Gesture; Identification; Smartphone; Authentication
• ISSN: 0167-4048; 1872-6208
• DOI: 10.1016/j.cose.2021.102462

This paper focuses on how zoom touchscreen gestures can be used to continuously authenticate and identify smartphone users. The zoom gesture is critically under-researched as a behavioral biometric despite richness of data found in this gesture. Furthermore, analysing how the zoom gesture performs over time is a novel line of inquiry. Zoom samples from three different data collection sessions were sourced. In these sessions, each participant zoomed in and out on three images. Eighty-five features were extracted from each gesture. The classification models used were Support Vector Machine (SVM), Random Forest (RF), and K-nearest Neighbor (KNN). The best authentication performance of AUC 0.937 and EER 10.6% were achieved using the SVM classifier. The best identification performance of 65.5% accuracy, 69.6% precision, and 67.9% recall were achieved using the RF classifier. In terms of stability over time, SVM proved to be the most stable classifier, with an AUC degradation of only 0.007 after two weeks had elapsed. This analysis proves that zoom gestures demonstrate promise for use in continuous smartphone authentication and identification applications.