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Accurate ROI localization and hierarchical hyper-sphere model for finger-vein recognition.
- Source :
-
Neurocomputing . Feb2019, Vol. 328, p171-181. 11p. - Publication Year :
- 2019
-
Abstract
- Abstract Recently, the finger-vein (FV) trait has attracted substantial attentions for personal recognition in biometric community, and some FV-based biometric systems have been well developed in real applications. However, improving the efficiency of FV recognition over a large-scale database remains a big practical problem. Moreover, unreliable finger-vein region of interest (ROI) localization and venous region enhancement can also heavily degrade the performance of a finger-vein based recognition system in practical scenario. In this paper, we first propose some new methods in FV ROI extraction and enhancement, and then an efficient and powerful hierarchical hyper-sphere model (HHsM) is developed based on granular computing (GrC). For HHsM construction, FV image samples from a given FV database are first converted into atomic granules for primary hyper-sphere granule set generation, and then some coarsened granule sets with different granularity levels are born by hyper-sphere granulation. Considering recognition efficiency improvement, a new hierarchical relationship among the coarsened granule sets is established to structure them level-wisely. Experimental results demonstrate that the proposed methods perform very well in handling ROI extraction, venous region enhancement and FV recognition. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 328
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 134252812
- Full Text :
- https://doi.org/10.1016/j.neucom.2018.02.098