Gesturebased User Identity Verification as an Open Set Problem for Smartphones The most straightforward, yet insecure, methods of authenticating a person on smartphones derive from the solutions applied to personal computers or smart cards, namely the authorization by passwords or numeric codes. Alarmingly, the widespread use of smartphone platforms implies that people are carrying around sensitive information in their pocket, making the information more available physically. As smartphone owners are often using their devices in public areas, these short numeric codes or other forms of passwords can be obtained quickly through shoulder surfing, resulting in making that restricted data far more accessible for those who are not authorized to access the device. In this paper, we address the problem of biometric verification on smartphones. We propose a new approach for gesturebased verification that makes use of open set recognition algorithms. Further, we introduce a new database of inertial measurements to investigate the user identification capabilities of this approach. The results we have obtained indicate that this approach is a feasible solution, although the precision of the method depends highly on the chosen samples of the training set.

Authenticating Phone Users Using a GaitBased Histogram Approach on Mobile App Sessions Collectively, userfriendly interfaces, small but impactful sensing technologies, intuitive device designs, and the variety of mobile applications (or apps) have transformed the expectations for cellular phones. Apps are a primary factor in device functionality; they allow users to quickly carry out tasks directly on their device. This paper leverages mobile apps for continuous authentication of mobile device users. We borrow from a gaitbased approach by continuously extracting nbin histograms from numerically encoded app data. Since more active subjects will generate more data, it would be trivial to distinguish between these subjects and others which are not as active. Thus, we divided a dataset of 19 months of app data from 181 subjects into three datasets to determine if minimally active, moderately active, or very active subjects were more challenging to authenticate. Using the absolute distance between two histograms, our approach yielded a worstcase EER of 0.188 and a bestcase EER of 0.036 with a worstcase initial training period of 1.06 hours. We also show a positive correlation between user activity level and performance, and template size and performance. Our method is characterized by minimal training samples and a contextindependent evaluation, addressing important factors which are known to affect the practicality of continuous authentication systems.