# Fingerprint Pore Detection: A Survey

arXiv: https://arxiv.org/abs/2211.14716 · HTML: https://mlsanity.com/paper/2211.14716/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 72 references that are in the map.
- First posted on arXiv 27 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [On the Expressive Power of Deep Neural Networks](https://mlsanity.com/paper/1606.05336/) (2016)
- [Learning non-maximum suppression](https://mlsanity.com/paper/1705.02950/) (2017)
- [Fully Transformer Networks for Semantic Image Segmentation](https://mlsanity.com/paper/2106.04108/) (2021)
- [A Comprehensive Survey of Transformers for Computer Vision](https://mlsanity.com/paper/2211.06004/) (2022)
- [Level Three Synthetic Fingerprint Generation](https://mlsanity.com/paper/2002.03809/) (2020)
- [Pore detection in high-resolution fingerprint images using Deep Residual Network](https://mlsanity.com/paper/1809.01986/) (2018)
- [Improving Fingerprint Pore Detection with a Small FCN](https://mlsanity.com/paper/1811.06846/) (2018)
- [PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition](https://mlsanity.com/paper/1905.06981/) (2019)
- [Unsupervised Domain Adaptation for Cross-sensor Pore Detection in High-resolution Fingerprint Images](https://mlsanity.com/paper/1908.10701/) (2019)

## 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