Deep Learning-Inspired Automatic Minutiae Extraction from Semi-Automated Annotations
Author:
Affiliation:
1. College of Mathematics and System Science, Xinjiang University
2. SEIEE of SJTU
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transfun/E107.A/9/E107.A_2024EAP1043/_pdf
Reference35 articles.
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2. [2] J. Sang, H. Wang, Q. Qian, H. Wu, and Y. Chen, “An efficient fingerprint identification algorithm based on minutiae and invariant moment,” Pers. Ubiquit Comput., vol.22, no.1, pp.71-80, 2018. 10.1007/s00779-017-1094-1
3. [3] T. Chugh, K. Cao, and A.K. Jain, “Fingerprint spoof buster: Use of minutiae-centered patches,” IEEE Trans. Inf. Forensics Secur., vol.13, no.9, pp.2190-2202, 2018. 10.1109/tifs.2018.2812193
4. [4] J.J. Engelsma, K. Cao, and A.K. Jain, “Learning a fixed-length fingerprint representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol.43, no.6, pp.1981-1997, 2021. 10.1109/tpami.2019.2961349
5. [5] A.K. Jain, D. Deb, and J.J. Engelsma, “Biometrics: Trust, but verify,” IEEE Trans. Inf. Forensics Secur., vol.4, no.3, pp.303-323, 2022. 10.1109/tbiom.2021.3115465
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