Super-resolution Guided Pore Detection for Fingerprint Recognition

TL;DR

This paper introduces a joint super-resolution and pore detection framework that enhances low-resolution fingerprint images to improve pore-based recognition accuracy, combining GANs with ridge and feature-based losses.

Contribution

It proposes a novel joint learning approach using a modified SRGAN and deep feature integration to recover high-resolution fingerprint details from low-resolution images.

Findings

High recognition accuracy with synthesized high-res fingerprints close to original high-res images.

Effective pore detection on low-resolution images after super-resolution enhancement.

Improved fingerprint recognition performance using combined super-resolution and pore detection.

Abstract

Performance of fingerprint recognition algorithms substantially rely on fine features extracted from fingerprints. Apart from minutiae and ridge patterns, pore features have proven to be usable for fingerprint recognition. Although features from minutiae and ridge patterns are quite attainable from low-resolution images, using pore features is practical only if the fingerprint image is of high resolution which necessitates a model that enhances the image quality of the conventional 500 ppi legacy fingerprints preserving the fine details. To find a solution for recovering pore information from low-resolution fingerprints, we adopt a joint learning-based approach that combines both super-resolution and pore detection networks. Our modified single image Super-Resolution Generative Adversarial Network (SRGAN) framework helps to reliably reconstruct high-resolution fingerprint samples from low-resolution ones assisting the pore detection network to identify pores with a high accuracy. The network jointly learns a distinctive feature representation from a real low-resolution fingerprint sample and successfully synthesizes a high-resolution sample from it. To add discriminative information and uniqueness for all the subjects, we have integrated features extracted from a deep fingerprint verifier with the SRGAN quality discriminator. We also add ridge reconstruction loss, utilizing ridge patterns to make the best use of extracted features. Our proposed method solves the recognition problem by improving the quality of fingerprint images. High recognition accuracy of the synthesized samples that is close to the accuracy achieved using the original high-resolution images validate the effectiveness of our proposed model.