Study Material

A Novel Fingerprint Matching Algorithm Based on Minutiae and

Global Statistical Features

Peng Shi, Jie Tian, Senior Member, IEEE, Qi Su, and Xin Yang

Abstract-The performance of Automated Fingerprint

Identification System (AFIS) is highly defined by the similarity

of effective features in fingerprints. Minutia is one of the most

widely used local features in fingerprint matching. In this paper,

we introduced two global statistical features of fingerprint

image, including the mean ridge width and the normalized

quality estimation of the whole image, and proposed a novel

fingerprint matching algorithm based on minutiae sets

combined with the global statistical features. The algorithm

proposed in this paper has the advantage of both local and

global features in fingerprint matching. It can improve the

accuracy of similarity measure without increasing of time and

memory consuming. Experimental results on FVC2004

databases showed that these improvements can make a better

matching performance on public domain databases.

I. INTRODUCTION

B ECAUSE of the stability and uniqueness, fingerprint is

widely used in biometric identification. The matching

method is one of the most crucial technologies in the

Automated Fingerprint Identification System (AFIS).

Whether two fingerprints are matched relies on the similarity

measure between the effective features of them. There are

mainly two kinds of features used in fingerprint matching:

local features and global features. Two most prominent local

ridge characteristics, called minutiae, are ridge ending and

ridge bifurcation [1]. Minutiae are the most widely used

features in the matching process. In the minutia-based

matching algorithms [2,3,4], the matching score calculations

Manuscript received August 18, 2007. This work is supported by the

Project ofNational Science Fund for Distinguished Young Scholars ofChina

under Grant No. 60225008, the Key Project of National Natural Science

Foundation of...