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Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection

Peralta, Daniel; Triguero, Isaac; Garc�a, Salvador; Saeys, Yvan; Benitez, Jose M.; Herrera, Francisco

Authors

Daniel Peralta

Salvador Garc�a

Yvan Saeys

Jose M. Benitez

Francisco Herrera



Abstract

Fingerprint recognition has been a hot research topic along the last few decades, with many applications and ever growing populations to identify. The need of flexible, fast identification systems is therefore patent in such situations. In this context, fingerprint classification is commonly used to improve the speed of the identification. This paper proposes a complete identification system with a hierarchical classification framework that fuses the information of multiple feature extractors. A feature selection is applied to improve the classification accuracy. Finally, the distributed identification is carried out with an incremental search, exploring the classes according to the probability order given by the classifier. A single parameter tunes the trade-off between identification time and accuracy. The proposal is evaluated over two NIST databases and a large synthetic database, yielding penetration rates close to the optimal values that can be reached with classification, leading to low identification times with small or no accuracy loss.

Citation

Peralta, D., Triguero, I., García, S., Saeys, Y., Benitez, J. M., & Herrera, F. (2017). Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection. Knowledge-Based Systems, 126, https://doi.org/10.1016/j.knosys.2017.03.014

Journal Article Type Article
Acceptance Date Mar 18, 2017
Online Publication Date Mar 22, 2017
Publication Date Jun 15, 2017
Deposit Date Mar 24, 2017
Publicly Available Date Mar 29, 2024
Journal Knowledge-Based Systems
Print ISSN 0950-7051
Electronic ISSN 1872-7409
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 126
DOI https://doi.org/10.1016/j.knosys.2017.03.014
Keywords Fingerprint recognition; Fingerprint identification; Fingerprint classification; Large databases; Feature selection; Hierarchical classification
Public URL https://nottingham-repository.worktribe.com/output/866014
Publisher URL http://www.sciencedirect.com/science/article/pii/S095070511730134X

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