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.
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