Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection
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...
| Main Authors: | , , , , , |
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/41561/ |
| _version_ | 1848796303067185152 |
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| author | Peralta, Daniel Triguero, Isaac García, Salvador Saeys, Yvan Benitez, Jose M. Herrera, Francisco |
| author_facet | Peralta, Daniel Triguero, Isaac García, Salvador Saeys, Yvan Benitez, Jose M. Herrera, Francisco |
| author_sort | Peralta, Daniel |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:45:50Z |
| format | Article |
| id | nottingham-41561 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:45:50Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-415612020-05-04T18:50:06Z https://eprints.nottingham.ac.uk/41561/ Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection Peralta, Daniel Triguero, Isaac García, Salvador Saeys, Yvan Benitez, Jose M. Herrera, Francisco 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. Elsevier 2017-06-15 Article PeerReviewed Peralta, Daniel, Triguero, Isaac, García, Salvador, Saeys, Yvan, Benitez, Jose M. and Herrera, Francisco (2017) Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection. Knowledge-Based Systems, 126 . pp. 91-103. ISSN 1872-7409 Fingerprint recognition; Fingerprint identification; Fingerprint classification; Large databases; Feature selection; Hierarchical classification http://www.sciencedirect.com/science/article/pii/S095070511730134X doi:/10.1016/j.knosys.2017.03.014 doi:/10.1016/j.knosys.2017.03.014 |
| spellingShingle | Fingerprint recognition; Fingerprint identification; Fingerprint classification; Large databases; Feature selection; Hierarchical classification Peralta, Daniel Triguero, Isaac García, Salvador Saeys, Yvan Benitez, Jose M. Herrera, Francisco Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection |
| title | Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection |
| title_full | Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection |
| title_fullStr | Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection |
| title_full_unstemmed | Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection |
| title_short | Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection |
| title_sort | distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection |
| topic | Fingerprint recognition; Fingerprint identification; Fingerprint classification; Large databases; Feature selection; Hierarchical classification |
| url | https://eprints.nottingham.ac.uk/41561/ https://eprints.nottingham.ac.uk/41561/ https://eprints.nottingham.ac.uk/41561/ |