一种基于新特征的有效指纹图像分割算法  

Effective Method for the Segmentation of Fingerprint Images Based on New Feature

作　　者：梅园[1] 曹国[1] 孙怀江[1] 孙权森[1] 夏德深[1] 

机构地区：[1]南京理工大学计算机科学与技术学院,南京210094

出　　处：《计算机科学》2009年第11期273-278,共6页Computer Science

基　　金：国家自然科学基金(60773172);江苏省自然科学基金(BK2008411);中国博士后基金(20070411055)资助

摘　　要：指纹图像分割在自动指纹识别系统中发挥了非常重要的作用,有效的分割不但可以减少后续处理的时间,而且可以大大增强特征提取的可靠性,提高系统识别的准确性。主要做了两个方面的工作:提出了一种称之为有效点聚集度的新的指纹图像分割特征;依据有效点聚集度及文献[1]中提出的块聚集度特征,提出了一种有效的指纹图像分割方法,该方法首先采用有效点聚集度对指纹图像做粗分割,然后对粗分割结果采用基于迭代的方法进行后处理,接着运用块聚集度在第一次后处理结果的基础上做细分割,最后采用形态学方法对细分割后的结果做第二次后处理。大量实验证明:相对于已有常用的指纹图像分割特征,有效点聚集度具有鉴别能力强、鲁棒性好、分割出的前景、背景区域较为集中的特点;基于有效点聚集度及块聚集度提出的指纹图像分割算法具有较高的准确性及较强的适应性。The segmentation of fingerprint images plays a very important role in Automatic Fingerprint Identification System （AFIS）. Effective segmentation can not only reduce the time of subsequent processing, but also improve the reliability of feature extraction considerably. This paper mainly contains two works： proposed a new segmentation feature for fingerprint images which is called Effective Point Cluster Degree （EPCD） ;proposed an effective method for the segmentation of fingerprint images based on the new feature and CIuD mentioned in reference[I], in this new method,we first segmented fingerprint images with EPCD, and then processed the first segmentation results with iteration-based me thod,did the second segmentation with CLuD based on the processed results, and finally, morphology was applied as postprocessing to reduce misclassifieation. All experimental results show that：compared with other commonly used features, the EPCD holds the characteristics of good discriminability, robustness, and the segmented foreground and background are more concentrated; at the same time, the segmented method based on EPCD and CluD possesses high accuracy and adaptability.

关 键 词：生物识别技术 自动指纹识别系统 指纹图像分割 特征提取 有效点聚集度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]