An increasing performance of fingerprint image segmentation based on clustering and algorithms

Abstract

The fingerprint identification system is nowadays the biometric sector that is most exploited. Segmentation of the fingerprint image is considered as one of its first stage of processing.This stage thus typically affects the extraction and matching process of the feature, resulting in a high accuracy fingerprint recognition system.Three important steps are proposed in this paper. First, to improve the quality of the fingerprint images, Soble and TopHat filtering method were used.K-means clustering for combining 5-dimensional vector characteristics (variance, mean difference, gradient coherence, ridge direction, and energy spectrum) then accurately separates the foreground and background region for each local block in a fingerprint image.Also, local variance thresholding is used in our approach to reducing computing time for segmentation. Finally, we are combined with our DBSCAN clustering system that was performed to overcome the disadvantages of classifying K-means in the segmentation of fingerprint images.In four different databases, the proposed algorithm is tested. Experimental results show that our approach is significantly effective in the separation between the ridge and non-ridge region against some recently published techniques.