@inproceedings{629249c700b74622be103a008e3f9b81,
title = "Feature Guided Fingerprint Pore Matching",
abstract = "The huge number of sweat pores in fingerprint images results in low efficiency of direct pore (DP) matching methods. To overcome this drawback, this paper proposes a feature guided fingerprint pore matching method. It selects “distinctive” pores around the minutiae and singular points from fingerprint images which extremely reduced the number of pore features for matching. And then, the selected “distinctive” pores are matched using the-state-of-the-art DP matching methods. We also consider to take the select “distinctive” pores together with the extracted minutiae and singular points as a whole feature set for matching. The experimental results have shown that the matching time of the proposed method can be reduced to a quarter of the original time when the recognition accuracy is kept at the same level. Both of the matching time and recognition accuracy are improved when multi-features are taken as a whole set for matching.",
keywords = "Fingerprint recognition, Pore matching, “Distinctive” pores",
author = "Feng Liu and Yuanhao Zhao and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 12th Chinese Conference on Biometric Recognition, CCBR 2017 ; Conference date: 28-10-2017 Through 29-10-2017",
year = "2017",
doi = "10.1007/978-3-319-69923-3_36",
language = "English",
isbn = "9783319699226",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "334--343",
editor = "Yunhong Wang and Yu Qiao and Jie Zhou and Jianjiang Feng and Zhenan Sun and Zhenhua Guo and Shiguang Shan and Linlin Shen and Shiqi Yu and Yong Xu",
booktitle = "Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings",
address = "Germany",
}