Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Fusion, Feature-Level

  • Arun Ross
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_157



In feature-level fusion, the feature sets originating from multiple biometric sources are consolidated into a single feature set by the application of appropriate feature normalization, transformation, and reduction schemes. The primary benefit of feature-level fusion is the detection of correlated feature values generated by different biometric algorithms thereby identifying a compact set of salient features that can improve recognition accuracy. Eliciting this feature set typically requires the use of  dimensionality reduction methods and, therefore, feature-level fusion assumes the availability of a large number of training data. Feature-level fusion algorithms can also be used for template update or template improvement.


Feature level fusion is an example of an early fusion strategy, i.e., the biometric evidence from multiple sources are consolidated beforeinvoking the matcher. In this scheme, multiple feature sets are integrated in...

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© Springer Science+Business Media, LLC 2009

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  • Arun Ross

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