@inproceedings{b93c685f608d452e9c55e5261ead3357,
title = "Fingerprint pore extraction using convolutional neural networks and logical operation",
abstract = "Sweat pores have been proved to be discriminative and successfully used for automatic fingerprint recognition. It is crucial to extract pores precisely to achieve high recognition accuracy. To extract pores accurately and robustly, we propose a novel coarse-to-fine detection method based on convolutional neural networks (CNN) and logical operation. More specifically, pore candidates are coarsely estimated using logical operation at first; then, coarse pore candidates are further judged through well-trained CNN models; precise pore locations are finally refined by logical and morphological operation. The experimental results evaluated on the public dataset show that the proposed method outperforms other state-of-the-art methods in comparison.",
keywords = "Convolutional neural network, Logical operation, Pore extraction",
author = "Yuanhao Zhao and Feng Liu and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 13th Chinese Conference on Biometric Recognition, CCBR 2018 ; Conference date: 11-08-2018 Through 12-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97909-0_5",
language = "English",
isbn = "9783319979083",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "38--47",
editor = "Zhenan Sun and Shiguang Shan and Zhenhong Jia and Kurban Ubul and Jie Zhou and Jianjiang Feng and Zhenhua Guo and Yunhong Wang",
booktitle = "Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings",
address = "Germany",
}