Multi-source human identification
Author(s)
Kim, Brian A. (Brian Andrew), 1979-
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Other Contributors
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Tomaso Poggio.
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In this thesis, a multi-source system for human identification is developed. The system uses three sources: face classifier, height classifier, and color classifier. In the process of developing this system, classifier combination and the integration of classifer outputs over sequences of data points were studied in detail. The method of classifier combination used relies on weighing classifiers based on the Maximum Likelihood estimation of class probabilities. The integration of classifer outputs, which is termed "temporal integration" in this thesis, has been developed to take advantage of the information implicitly contained in data correlated through time. In all experiments performed, temporal integration has improved classification, up to 40% in some cases. Meanwhile, the method of temporally integrating the outputs of multiple classifiers fused using our classifier weighting method outperforms all individual classifiers in the system.
Description
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003. Includes bibliographical references (p. 59-60).
Date issued
2003Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.