UFQA: Utility guided Fingerphoto Quality Assessment

TL;DR

This paper introduces UFQA, a self-supervised dual encoder framework for assessing the quality of fingerphotos, improving biometric recognition reliability by predicting utility and local quality, outperforming existing metrics.

Contribution

The paper presents a novel self-supervised dual encoder model for fingerphoto quality assessment that incorporates utility and local quality, tailored for contactless biometric scenarios.

Findings

UFQA outperforms NFIQ2.2 and other state-of-the-art algorithms.

The approach improves fingerphoto matching accuracy.

Experimental results validate the effectiveness of the proposed method.

Abstract

Quality assessment of fingerprints captured using digital cameras and smartphones, also called fingerphotos, is a challenging problem in biometric recognition systems. As contactless biometric modalities are gaining more attention, their reliability should also be improved. Many factors, such as illumination, image contrast, camera angle, etc., in fingerphoto acquisition introduce various types of distortion that may render the samples useless. Current quality estimation methods developed for fingerprints collected using contact-based sensors are inadequate for fingerphotos. We propose Utility guided Fingerphoto Quality Assessment (UFQA), a self-supervised dual encoder framework to learn meaningful feature representations to assess fingerphoto quality. A quality prediction model is trained to assess fingerphoto quality with additional supervision of quality maps. The quality metric is a predictor of the utility of fingerphotos in matching scenarios. Therefore, we use a holistic approach by including fingerphoto utility and local quality when labeling the training data. Experimental results verify that our approach performs better than the widely used fingerprint quality metric NFIQ2.2 and state-of-the-art image quality assessment algorithms on multiple publicly available fingerphoto datasets.