A Robust Fingerprint Matching Method

Jin Qi, Zhongchao Shi, Xuying Zhao, Yangsheng Wang

Abstract: This paper is concerned with an accurate and efficient fingerprint matching method. We have two main contributions: 1. we define a novel feature vector for each fingerprint minutia based on the global orientation field estimated from the fingerprint impression. These features are used to identify corresponding minutiae between two fingerprint impressions by computing the Euclidean distance between these feature vectors. 2. a novel distortion-tolerant matching algorithm based on the closest triangle is developed. Furthermore, the fingerprint directional field is also used to compute the final matching score combining with minutiae elaborately. A series of experiments conducted on the public data collection, FVC2002 DB3 set A (800 fingerprints), demonstrates the effectiveness of our method.