Research in keystroke dynamics has largely focused on the typing patterns found in fixed text (e.g. userid and passwords). In this regard, digraphs and trigraphs have proven to be discriminative features. However, there is increasing interest in free-text keystroke dynamics, in which the user to be authenticated is free to type whatever he/she wants, rather than a pre-determined text. The natural question that arises is whether digraphs and trigraphs are just as discriminative for free text as they are for fixed text. We attempt to answer this question in this paper. We show that digraphs and trigraphs, if computed without regard to what word was typed, are no longer discriminative. Instead, word-specific digraphs/trigraphs are required. We also show that the typing dynamics for some words depend on whether they are part of a larger word. Our study is the first to investigate these issues, and we hope our work will help guide researchers looking for good features for free-text keystroke dynamics.

Traditionally, people were either producers of entertainment media, or else consumers of them. Today's digital entertainment, however, provides for a new dimension: that of interactivity. Instead of passive enjoyment, consumers can now control some elements of the media that were previously solely determined by the producer. This interactivity appears to enhance enjoyment. In this paper, we present a vision-based, interactive music playback system which allows anyone, even untrained musicians, to conduct music. The goal is to allow the user to dynamically influence how music is played back, much like what a real conductor would do. The tempo and volume of the music playback are controlled by the user's movements. In addition, our system projects colorful patterns that respond to the user, making the interaction truly multimedia

Conventional verification systems, such as those controlling access to a secure room, do not usually require the user to reauthenticate himself for continued access to the protected resource. This may not be sufficient for high-security environments in which the protected resource needs to be continuously monitored for unauthorized use. In such cases, continuous verification is needed. In this paper, we present the theory, architecture, implementation, and performance of a multimodal biometrics verification system that continuously verifies the presence of a logged-in user. Two modalities are currently used--face and fingerprint--but our theory can be readily extended to include more modalities. We show that continuous verification imposes additional requirements on multimodal fusion when compared to conventional verification systems. We also argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for continuous verification and propose new metrics against which we benchmark our system.

In this paper we describe the theory, architecture, implementation, and performance of a multimodal passive biometric verification system that continually verifies the presence/participation of a logged-in user. We assume that the user logged in using strong authentication prior to the starting of the continuous verification process. While the implementation described in the paper combines a digital camera-based face verification with a mouse-based fingerprint reader, the architecture is generic enough to accommodate additional biometric devices with different accuracy of classifying a given user from an imposter. The main thrust of our work is to build a multimodal biometric feedback mechanism into the operating system so that verification failure can automatically lock up the computer within some estimate of the time it takes to subvert the computer. This must be done with low false positives in order to realize a usable system. We show through experimental results that combining multiple suitably chosen modalities in our theoretical framework can effectively do that with currently available off-the-shelf components

In this paper, we focus on the use of context-aware, collaborative filtering, machine-learning techniques that leverage automatically sensed and inferred contextual metadata together with computer vision analysis of image content to make accurate predictions about the human subjects depicted in cameraphone photos. We apply Sparse-Factor Analysis (SFA) to both the contextual metadata gathered in the MMM2 system and the results of PCA (Principal Components Analysis) of the photo content to achieve a 60% face recognition accuracy of people depicted in our cameraphone photos, which is 40% better than media analysis alone. In short, we use context-aware media analysis to solve the face recognition problem for cameraphone photos.