Fingerprint Pore Detection: A Survey

TL;DR

This survey reviews fingerprint pore detection methods, introduces a baseline CNN approach, reimplements existing methods, and provides open-source code to facilitate future research and standardized evaluation.

Contribution

It is the first comprehensive survey on fingerprint pore detection, offering a baseline method, reimplementations, and publicly available code for benchmarking.

Findings

Baseline method achieves optimal pore detection rates.

Reimplemented approaches provide comparative benchmarks.

Open-source code promotes reproducibility and future research.

Abstract

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