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Error weighted classifier combination for multi-modal human identification

Author(s)
Ivanov, Yuri; Serre, Thomas; Bouvrie, Jacob
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Abstract
In this paper we describe a technique of classifier combination used in a human identification system. The system integrates all available features from multi-modal sources within a Bayesian framework. The framework allows representinga class of popular classifier combination rules and methods within a single formalism. It relies on a “per-class” measure of confidence derived from performance of each classifier on training data that is shown to improve performance on a synthetic data set. The method is especially relevant in autonomous surveillance setting where varying time scales and missing features are a common occurrence. We show an application of this technique to the real-world surveillance database of video and audio recordings of people collected over several weeks in the office setting.
Date issued
2005-12-14
URI
http://hdl.handle.net/1721.1/30590
Other identifiers
MIT-CSAIL-TR-2005-081
AIM-2005-035
CBCL-258
Series/Report no.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Keywords
AI, classifier combination, face recognition, identification, multi-modal

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