计算机科学
聚类分析
人工智能
模式识别(心理学)
数据库扫描
分割
k均值聚类
计算机视觉
指纹(计算)
图像分割
模糊聚类
数据挖掘
作者
El Mehdi Cherrat,Rachid Alaoui,Hassane Bouzahir
出处
期刊:International Journal of Electrical and Computer Engineering
[Institute of Advanced Engineering and Science]
日期:2019-08-01
卷期号:9 (4): 2425-2432
被引量:4
标识
DOI:10.11591/ijece.v9i4.pp2425-2432
摘要
Nowadays, the fingerprint identification system is the most exploited sector of biometric. Fingerprint image segmentation is considered one of its first processing stage. Thus, this stage affects typically the feature extraction and matching process which leads to fingerprint recognition system with high accuracy. In this paper, three major steps are proposed. First, Soble and TopHat filtering method have been used to improve the quality of the fingerprint images. Then, for each local block in fingerprint image, an accurate separation of the foreground and background region is obtained by K-means clustering for combining 5-dimensional characteristics vector (variance, difference of mean, gradient coherence, ridge direction and energy spectrum). Additionally, in our approach, the local variance thresholding is used to reduce computing time for segmentation. Finally, we are combined to our system DBSCAN clustering which has been performed in order to overcome the drawbacks of K-means classification in fingerprint images segmentation. The proposed algorithm is tested on four different databases. Experimental results demonstrate that our approach is significantly efficacy against some recently published techniques in terms of separation between the ridge and non-ridge region.
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