User interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures, which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life usage of a mobile device consists of several unconstrained scenarios as well. Thus, our work aims to evaluate the stability of swipe gesture authentication across various usage scenarios of a mobile device. The evaluations were performed using state-of-the-art touch-based classification algorithms—support vector machine (SVM), k-nearest neighbour (kNN) and naive Bayes—to evaluate the robustness of swipe gestures across device usage scenarios. To simulate real-life behaviour, multiple usage scenarios covering stationary and dynamic modes are considered for the analysis. Additionally, we focused on analysing the stability of verification accuracy for time-separated swipes by performing intra-session (acquired on the same day) and inter-session (swipes acquired a week later) comparisons. Finally, we assessed the consistency of individual features for horizontal and vertical swipes using a statistical method. Performance evaluation results indicate impact of body movement and environment (indoor and outdoor) on the user verification accuracy. The results reveal that for a static user scenario, the average equal error rate is 1%, and it rises significantly for the scenarios involving any body movement—caused either by user or the environment. The performance evaluation for time-separated swipes showed better verification accuracy rate for swipes acquired on the same day compared to swipes separated by a week. Finally, assessment on feature consistency reveal a set of consistent features such as maximum slope, standard deviation and mean velocity of second half of stroke for both horizontal and vertical swipes. The performance evaluation of swipe-based authentication shows variation in verification accuracy across different device usage scenarios. The obtained results challenge the adoption of swipe-based authentication on mobile devices. We have suggested ways to further achieve stability through specific template selection strategies. Additionally, our evaluation has established that at least 6 swipes are needed in enrolment to achieve acceptable accuracy. Also, our results conclude that features such as maximum slope and standard deviation are the most consistent features across scenarios.

中文翻译：

---

评估移动设备使用场景中的滑动手势身份验证的稳定性

用户与移动设备的交互主要由触摸动作（也称为轻扫手势）组成，这些动作用作行为生物特征验证用户的身份。文献揭示了有前途的滑动手势身份验证的验证准确率。现有的大多数研究在其实验设置中都考虑了受限的环境。但是，移动设备的实际使用情况也包括几种不受约束的情况。因此，我们的工作旨在评估移动设备各种使用场景下的滑动手势身份验证的稳定性。使用基于触摸的最新分类算法（支持向量机（SVM），k最近邻（kNN）和朴素贝叶斯）进行评估，以评估跨设备使用场景的轻扫手势的鲁棒性。为了模拟现实生活中的行为，分析中考虑了涵盖固定模式和动态模式的多种使用场景。此外，我们专注于通过进行会话内（同一天获取）和会话间（一周后获取的刷卡）比较来分析时间分隔的刷卡验证准确性的稳定性。最后，我们使用统计方法评估了水平和垂直滑动单个特征的一致性。性能评估结果表明人体运动和环境（室内和室外）对用户验证准确性的影响。结果表明，对于静态用户场景，平均均等错误率为1％，并且在涉及任何由用户或环境引起的身体移动的场景中，平均均等错误率会大大提高。与按一周分隔的抽签相比，按时间划分的抽签的性能评估显示，同一天获取的抽签的验证准确率更高。最后，对特征一致性的评估揭示了一组一致的特征，例如水平和垂直滑动的最大斜率，标准偏差和笔触后半部分的平均速度。基于滑动的身份验证的性能评估显示了在不同设备使用情况下验证准确性的差异。获得的结果对在移动设备上采用基于滑动的身份验证提出了挑战。我们提出了通过特定模板选择策略进一步实现稳定性的方法。此外，我们的评估已经确定，至少需要进行6次滑动才能获得可接受的准确性。也，