Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning

TL;DR

This paper introduces a recurrent adversarial learning approach to develop a sensor-invariant fingerprint ROI segmentation model, reducing the need for re-training and manual annotation when new sensors are used.

Contribution

The paper proposes a novel recurrent adversarial learning framework for sensor-invariant fingerprint ROI segmentation, enhancing cross-sensor performance without manual ROI annotation.

Findings

Improved segmentation accuracy on unseen sensors.

Reduced need for manual ROI annotation.

Effective feature alignment across different sensors.

Abstract

A fingerprint region of interest (roi) segmentation algorithm is designed to separate the foreground fingerprint from the background noise. All the learning based state-of-the-art fingerprint roi segmentation algorithms proposed in the literature are benchmarked on scenarios when both training and testing databases consist of fingerprint images acquired from the same sensors. However, when testing is conducted on a different sensor, the segmentation performance obtained is often unsatisfactory. As a result, every time a new fingerprint sensor is used for testing, the fingerprint roi segmentation model needs to be re-trained with the fingerprint image acquired from the new sensor and its corresponding manually marked ROI. Manually marking fingerprint ROI is expensive because firstly, it is time consuming and more importantly, requires domain expertise. In order to save the human effort in generating annotations required by state-of-the-art, we propose a fingerprint roi segmentation model which aligns the features of fingerprint images derived from the unseen sensor such that they are similar to the ones obtained from the fingerprints whose ground truth roi masks are available for training. Specifically, we propose a recurrent adversarial learning based feature alignment network that helps the fingerprint roi segmentation model to learn sensor-invariant features. Consequently, sensor-invariant features learnt by the proposed roi segmentation model help it to achieve improved segmentation performance on fingerprints acquired from the new sensor. Experiments on publicly available FVC databases demonstrate the efficacy of the proposed work.