An ensemble approach to data fusion and its application to ATR

Costin Barbu; Jing Peng; Richard Sims

doi:10.1117/12.720156

7 May 2007 An ensemble approach to data fusion and its application to ATR

Costin Barbu, Jing Peng, Richard Sims

Proceedings Volume 6566, Automatic Target Recognition XVII; 65660O (2007) https://doi.org/10.1117/12.720156
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

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Costin Barbu, Jing Peng, and Richard Sims "An ensemble approach to data fusion and its application to ATR", Proc. SPIE 6566, Automatic Target Recognition XVII, 65660O (7 May 2007); https://doi.org/10.1117/12.720156

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KEYWORDS

Data fusion

Biometrics

Glasses

Matrices

Image fusion

Binary data

Databases

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