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.

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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.

Abstract

Abstract.It is well known that Keystroke Dynamics can be used as a biometric to authenticate users. But most work to date use fixed strings, such as userid or password. In this paper, we study the feasibility of using Keystroke Dynamics as a biometric in a more general setting, where users go about their normal daily activities of emailing, web surfing, and so on. We design two classifiers that appropriate for one-time and continuous authentication. We also propose a new Goodness Measure to compute the quality of a word used for Keystroke Dynamics. From our experiments we find that, surprisingly, non-English words are better suited for identification than English words.