Share Email Print

Proceedings Paper

An ensemble approach to data fusion and its application to ATR
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Ensemble methods provide a principled framework in which to build high performance classifiers and represent many types of data. As a result, these methods can be useful for making inferences about biometric and biological events. We introduce a novel ensemble method for combining multiple representations (or views). The method is a multiple view generalization of AdaBoost. Similar to AdaBoost, base classifiers are independently built from each represetation. Unlike AdaBoost, however, all data types share the same sampling distribution computed from the base classifier having the smallest error rate among input sources. As a result, the most consistent data type dominates over time, thereby significantly reducing sensitivity to noise. The method is applied to the problem of facial and gender prediction based on biometric traits. The new method outperforms several competing techniques including kernel-based data fusion, and is provably better than AdaBoost trained on any single type of data.

Paper Details

Date Published: 7 May 2007
PDF: 12 pages
Proc. SPIE 6566, Automatic Target Recognition XVII, 65660O (7 May 2007); doi: 10.1117/12.720156
Show Author Affiliations
Costin Barbu, MIT Lincoln Lab. (United States)
Jing Peng, Montclair State Univ. (United States)
Richard Sims, US Army RDECOM (United States)

Published in SPIE Proceedings Vol. 6566:
Automatic Target Recognition XVII
Firooz A. Sadjadi, Editor(s)

© SPIE. Terms of Use
Back to Top