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.

本文重点介绍如何使用缩放触摸屏手势来持续验证和识别智能手机用户。尽管在此手势中发现了丰富的数据，但缩放手势作为行为生物识别技术的研究不足。此外，分析缩放手势随着时间的推移如何执行是一个新的查询线。来自三个不同数据收集会话的缩放样本来源。在这些会议中，每个参与者都放大和缩小了三张图像。从每个手势中提取了 85 个特征。使用的分类模型是支持向量机 (SVM)、随机森林 (RF) 和 K-最近邻 (KNN)。使用 SVM 分类器实现了 AUC 0.937 和 EER 10.6% 的最佳认证性能。65.5% 准确率、69.6% 准确率和 67 的最佳识别性能。使用 RF 分类器实现了 9% 的召回率。就时间稳定性而言，SVM 被证明是最稳定的分类器，两周后 AUC 下降仅为 0.007。该分析证明缩放手势有望用于连续的智能手机身份验证和识别应用程序。