 In this paper, we present a novel technique for the tracking and identification of multiple persons in a smart environment using distantly placed audio-visual sensors. The technique builds on the opportunistic integration of tracking as well as face and voice identification cues, gained from several cameras and microphones, whenever these cues can be captured with a sufficient degree of confidence. A probabilistic model is used to keep track of identified persons and update the belief in their identities whenever new observations can be made. The technique has been systematically evaluated on the CLEAR Interactive Seminar database, a large audio-visual corpus of realistic meeting scenarios captured in a variety of smart rooms.
