Enhancing Security on TouchScreen Sensors with Augmented Handwritten Signatures We aim at enhancing personal identity security on mobile touchscreen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropybased data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touchscreen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature.

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.