# Pore detection in high-resolution fingerprint images using Deep Residual Network

arXiv: https://arxiv.org/abs/1809.01986 · HTML: https://mlsanity.com/paper/1809.01986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Fingerprint Pore Detection: A Survey](https://mlsanity.com/paper/2211.14716/) (2022)

## Abstract (arXiv)

This letter presents a residual learning-based convolutional neural network, referred to as DeepResPore, for detection of pores in high-resolution fingerprint images. Specifically, the proposed DeepResPore model generates a pore intensity map from the input fingerprint image. Subsequently, the local maxima filter is operated on the pore intensity map to identify the pore coordinates. The results of our experiments indicate that the proposed approach is effective in extracting pores with a true detection rate of 94:49% on Test set I and 93:78% on Test set II of the publicly available PolyU HRF dataset. Most importantly, the proposed approach achieves state-of-the-art performance on both test sets.