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

TL;DR

This paper introduces DeepResPore, a deep residual neural network that accurately detects pores in high-resolution fingerprint images, achieving state-of-the-art performance on benchmark datasets.

Contribution

The paper proposes a novel residual learning-based CNN model for pore detection in high-resolution fingerprints, improving detection accuracy over existing methods.

Findings

True detection rate of 94.49% on Test set I

True detection rate of 93.78% on Test set II

Achieves state-of-the-art performance on PolyU HRF dataset

Abstract

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.