# PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition

## TL;DR

This paper introduces PoreNet, a CNN-based method for high-resolution fingerprint recognition that detects pores and computes descriptors, achieving state-of-the-art accuracy on benchmark datasets.

## Contribution

The paper presents PoreNet, a novel residual CNN model for pore feature extraction, improving fingerprint recognition accuracy over existing methods.

## Key findings

- Achieves 2.91% and 0.57% EERs on benchmark datasets.
- Outperforms current state-of-the-art in FMR1000 and FMR10000.
- Effective pore-based biometric recognition in high-resolution fingerprints.

## Abstract

With the development of high-resolution fingerprint scanners, high-resolution fingerprint-based biometric recognition has received increasing attention in recent years. This paper presents a pore feature-based approach for biometric recognition. Our approach employs a convolutional neural network (CNN) model, DeepResPore, to detect pores in the input fingerprint image. Thereafter, a CNN-based descriptor is computed for a patch around each detected pore. Specifically, we have designed a residual learning-based CNN, referred to as PoreNet that learns distinctive feature representation from pore patches. For verification, the match score is generated by comparing pore descriptors obtained from a pair of fingerprint images in bi-directional manner using the Euclidean distance. The proposed approach for high-resolution fingerprint recognition achieves 2.91% and 0.57% equal error rates (EERs) on partial (DBI) and complete (DBII) fingerprints of the benchmark PolyU HRF dataset. Most importantly, it achieves lower FMR1000 and FMR10000 values than the current state-of-the-art approach on both the datasets.