Rajkumar Janakiraman

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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,(More)
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(More)
This paper describes a new approach to the automatic detection of human faces and places depicted in photographs taken on cameraphones. Cameraphones offer a unique opportunity to pursue new approaches to media analysis and management: namely to combine the analysis of automatically gathered contextual metadata with media content analysis to fundamentally(More)
In this paper we describe the theory, architecture, implementation , and performance of a multi-modal passive bio-metric verification system that continually verifies the pres-ence/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(More)
In this paper we describe the architecture, implementation, and performance of a face verification system that continually verifies the presence of a logged-in user at a computer console. It maintains a sliding window of about ten seconds of verification data points and uses them as input to a Bayesian framework to compute a probability that the logged-in(More)
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