This paper investigates multitouch gestures for user authentication on touch sensitive devices. A canonical set of 22 multitouch gestures was defined using characteristics of hand and finger movement. Then, a multitouch gesture matching algorithm robust to orientation and translation was developed. Two different studies were performed to evaluate the concept. First, a single session experiment was performed in order to explore feasibility of multitouch gestures for user authentication. Testing on the canonical set showed that the system could achieve good performance in terms of distinguishing between gestures performed by different users. In addition, the tests demonstrated a desirable alignment of usability and security as gestures that were more secure from a biometric point of view were rated as more desirable in terms of ease, pleasure, and excitement. Second, a study involving a three-session experiment was performed. Results indicate that biometric information gleaned from a short user-device interaction remains consistent across gaps of several days, though there is noticeable degradation of performance when the authentication is performed over multiple sessions. In addition, the study showed that user-defined gestures yield the highest recognition rate among all other gestures, whereas the use of multiple gestures in a sequence aids in boosting verification accuracy. In terms of memorability, the study showed that it is feasible for a user to recall user-defined gestural passwords and it is observed that the recall rate increases over time. It is also noticed that performing a user-defined gesture over a customized background image does result in higher verification performance. In terms of usability, the study shows that users did not have difficulty in performing multitouch gestures as they all rated each gesture as easy to perform.

In this paper, we present a novel multi-touch gesture-based authentication technique. We take advantage of the multi-touch surface to combine biometric techniques with gestural input. We defined a comprehensive set of five-finger touch gestures, based upon classifying movement characteristics of the center of the palm and fingertips, and tested them in a user study combining biometric data collection with usability questions. Using pattern recognition techniques, we built a classifier to recognize unique biometric gesture characteristics of an individual. We achieved a 90% accuracy rate with single gestures, and saw significant improvement when multiple gestures were performed in sequence. We found user ratings of a gestures desirable characteristics (ease, pleasure, excitement) correlated with a gestures actual biometric recognition rate - that is to say, user ratings aligned well with gestural security, in contrast to typical text-based passwords. Based on these results, we conclude that multi-touch gestures show great promise as an authentication mechanism.