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

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 24, 2017
• 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
• Contract Date: Mar 24, 2017