Error weighted classifier combination for multi-modal human identification
Author(s)Ivanov, Yuri; Serre, Thomas; Bouvrie, Jacob
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 Âper-classÂ 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.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
AI, classifier combination, face recognition, identification, multi-modal