2026-09-18T19:33:16Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/296622022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomaso Poggio.
Kim, Brian A. (Brian Andrew), 1979-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-03-24T16:12:55Z
2006-03-24T16:12:55Z
2003
2003
http://hdl.handle.net/1721.1/29662
53827351
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 59-60).
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.
by Brian A. Kim.
M.Eng.
60 p.
1820874 bytes
1820681 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Multi-source human identification
Thesis
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