Fingerprint Pore Detection: A Survey
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Abstract (arXiv)
This work presents the first survey on fingerprint pore detection. The survey provides a general overview of the field and discusses methods, datasets, and evaluation protocols. We also present a baseline method inspired on the state-of-the-art that implements a customizable Fully Convolutional Network, whose hyperparameters were tuned to achieve optimal pore detection rates. Finally, we also reimplementated three other approaches proposed in the literature for evaluation purposes. We have made the source code of (1) the baseline method, (2) the reimplemented approaches, and (3) the training and evaluation processes for two different datasets available to the public to attract more researchers to the field and to facilitate future comparisons under the same conditions. The code is available in the following repository: https://github.com/azimIbragimov/Fingerprint-Pore-Detection-A-Survey
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 72 references that are in the map. First posted on arXiv 27 Nov 2022.
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18 of 72 references.
References · 18
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6 more
- 2022A Comprehensive Survey of Transformers for Computer Visioncited by 13
- 2020Level Three Synthetic Fingerprint Generationcited by 12
- 2018Pore detection in high-resolution fingerprint images using Deep Residual Networkcited by 1
- 2018Improving Fingerprint Pore Detection with a Small FCNcited by 1
- 2019PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognitioncited by 1
- 2019Unsupervised Domain Adaptation for Cross-sensor Pore Detection in High-resolution Fingerprint Imagescited by 1
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