Abstract—We hypothesize that an individual computer user has a unique and consistent habitual pattern of hand movements, independent of the text, while typing on a keyboard. As a result, this paper proposes a novel biometric modality named “Typing Behavior (TB) ” for continuous user authentication. Given a webcam pointing toward a keyboard, we develop real-time computer vision algorithms to automatically extract hand movement patterns from the video stream. Unlike the typical continuous biometrics such as keystroke dynamics (KD), TB provides reliable authentication with a short delay, while avoiding explicit key-logging. We collect a video database where 63 unique subjects type static text and free text for multiple sessions. For one typing video, the hands are segmented in each frame and a unique descriptor is extracted based on the shape and position of hands, as well as their temporal dynamics in the video sequence. We propose a novel approach, named bag of multi-dimensional phrases, to match the cross-feature and cross-temporal pattern between a gallery sequence and a probe sequence. The experimental results demonstrate superior performance of TB when compared to KD, which, together with our ultra-real-time demo system, warrant further investigation of this novel vision application and biometric modality. Index Terms—Continuous authentication, user authentication, biometrics, typing behavior, hand movements, bag of phrases, bag of multi-dimensional phrases, keystroke dynamics, keyboard. I.

Abstract—The goal of this paper is to determine if keystroke sound can be used to recognize a user. In this regard, we analyze the discriminative power of keystroke sound in the context of a continuous user authentication application. Motivated by the concept of digraphs used in modeling keystroke dynamics, a virtual alphabet is first learned from keystroke sound segments. Next, the digraph latency within pairs of virtual letters, along with other statistical features, are used to generate match scores. The resultant scores are indicative of the similarities between two sound streams, and are fused to make a final authentication decision. Experiments on both static and free text-based authentication on a database of 50 subjects demonstrate the potential as well as limitations of keystroke sound. Index Terms—Keystroke sound, keystroke dynamics, keyboard typing, continuous authentication. I.