We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0 for intrasession authentication, 2 -3 for intersession authentication, and below 4 when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multimodal biometric authentication system.
@cite_16: Online dynamic signature verification systems were designed and tested. A database of more than 10,000 signatures in (x(t), y(t))-form was acquired using a graphics tablet. We extracted a 42-parameter feature set at first, and advanced to a set of 49 normalized features that tolerate inconsistencies in genuine signatures while retaining the power to discriminate against forgeries. We studied algorithms for selecting and perhaps orthogonalizing features in accordance with the availability of training data and the level of system complexity. For decision making we studied several classifiers types. A modified version of our majority classifier yielded 2.5 equal error rate and, more importantly, an asymptotic performance of 7 false acceptance rate at zero false rejection rate, was robust to the speed of genuine signatures, and used only 15 parameter features.
@cite_32: An on-line signature verification system exploiting both local and global information through decision-level fusion is presented. Global information is extracted with a feature-based representation and recognized by using Parzen Windows Classifiers. Local information is extracted as time functions of various dynamic properties and recognized by using Hidden Markov Models. Experimental results are given on the large MCYT signature database (330 signers, 16500 signatures) for random and skilled forgeries. Feature selection experiments based on feature ranking are carried out. It is shown experimentally that the machine expert based on local information outperforms the system based on global analysis when enough training data is available. Conversely, it is found that global analysis is more appropriate in the case of small training set size. The two proposed systems are also shown to give complementary recognition information which is successfully exploited using decision-level score fusion.
@cite_21: 
Touchalytics resembles the field of on-line signature authentication or on-line signature verification @cite_16 @cite_21 in that it extracts temporal features of human gestures on planar surfaces. @cite_16 , 49 temporal and geometric features are extracted from 5,603 signatures of 105 subjects. In newer contributions such as @cite_32 , pressure is also used to compute features. Most approaches achieve an equal error rate (EER) between 1 complexity of gestures differs between signature verification and touchalytics. Compared to touch strokes, signatures are rather complex, which enables the extraction of more sophisticated features that support the authentication task. The main differences are the tools used and the availability of data. While in touchalytics the recording of all kinds of raw features is implicit for this interaction and is therefore always available, in signature verification only the spatial features are available without explicitly augmenting either pen or paper with extra sensors. This renders touchalytics more suitable for continuous authentication in practice.