A Novel Fingerprint Matching Algorithm Based on Minutiae and Global Statistical Features

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.