A Novel High-Resolution Fingerprint Representation Method

Current high-resolution fingerprints are almost entirely represented by sweat pores, resulting in information loss and weak robustness. This is caused by dividing fingerprint representation into pore detection and pore representation. This paper proposes a novel high-resolution fingerprint representation method for recognition based on only one fully convolutional network. By guiding the network to learn the most discriminative representation (i.e., sweat pores) and to reconstruct the original fingerprint image, pore maps and hidden features are provided simultaneously to represent the high-resolution fingerprint image for the subsequent direct matching process. The experimental results, which are evaluated on the public PolyU pore database, show that the best average RT, which is 94.88%, can be obtained using our proposed method. In addition, the recognition results show that our proposed method achieves matching equal error rates (EERs) of 5.55% and 1.27% on the PolyU DBI and DBII databases, respectively, which is better than other pore detection methods when using the same matching method. Compared with the latest DeepPoreID method which split fingerprint representation into pore extraction and pore representation, our approach improves the EER by up to 50.98% when evaluated on an in-house database, further demonstrating the outstanding generalization ability of the proposed method.