An Efficient and Effective Model for Large Scale Fingerprint Matching

Fingerprint is one of the most widely used biometric features to identify a person. Fingerprint matching is to compare an input fingerprint with fingerprints within the database to find the most similar fingerprints. However, existing fingerprint matching technologies face two major challenges: data quantity and data quality. From the perspective of data quantity, the existing fingerprint database stores hundreds of millions of data, so the traditional iterative matching method is not practical due to huge time consumption. From the data quality point, existing fingerprint products have a small collection area for aesthetics and portability, resulting in a low quality of the captured finger-print image, which seriously affects the accuracy of the matching algorithms. To address the above problems, we present a two-stage fingerprint matching method, which not only improves the efficiency of fingerprint matching, but also ensures the accuracy of fingerprint matching. The first stage utilizes a convolutional neural network trained with triplet loss to extract the overall feature of a fingerprint image. The cosine similarity between features can be used to identify possible fingerprint matches. The second stage compares fingerprint matches acquired in the first stage using a minutiae points comparing algorithm to get a more precise result. The experiment result shows that our method is satisfactory in both speed and accuracy.