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Issue No. 10 - October (2011 vol. 33)
ISSN: 0162-8828
pp: 1925-1937
Behrooz Kamgar-Parsi , Naval Research Laboratory, Washington DC
Wallace Lawson , Naval Research Laboratory, Washington DC
Behzad Kamgar-Parsi , Office of Naval Research, Arlington
The interest in face recognition is moving toward real-world applications and uncontrolled sensing environments. An important application of interest is automated surveillance, where the objective is to recognize and track people who are on a watchlist. For this open world application, a large number of cameras that are increasingly being installed at many locations in shopping malls, metro systems, airports, etc., will be utilized. While a very large number of people will approach or pass by these surveillance cameras, only a small set of individuals must be recognized. That is, the system must reject every subject unless the subject happens to be on the watchlist. While humans routinely reject previously unseen faces as strangers, rejection of previously unseen faces has remained a difficult aspect of automated face recognition. In this paper, we propose an approach motivated by human perceptual ability of face recognition which can handle previously unseen faces. Our approach is based on identifying the decision region(s) in the face space which belong to the target person(s). This is done by generating two large sets of borderline images, projecting just inside and outside of the decision region. For each person on the watchlist, a dedicated classifier is trained. Results of extensive experiments support the effectiveness of our approach. In addition to extensive experiments using our algorithm and prerecorded images, we have conducted considerable live system experiments with people in realistic environments.
Face recognition, automatic surveillance, human-like classification, morphing facial images, biometrics, open world face recognition.

B. Kamgar-Parsi, W. Lawson and B. Kamgar-Parsi, "Toward Development of a Face Recognition System for Watchlist Surveillance," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1925-1937, 2011.
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