Fingerprint mosaicking, which is the process of combining multiple fingerprint images into a single master fingerprint, is an essential process in modern biometric systems. However, it is prone to errors that can significantly degrade fingerprint image quality. This paper proposes a novel deep learning-based approach to detect and score mosaicking artifacts in fingerprint images. Our method leverages a self-supervised learning framework to train a model on large-scale unlabeled fingerprint data, eliminating the need for manual artifact annotation. The proposed model effectively identifies mosaicking errors, achieving high accuracy on various fingerprint modalities, including contactless, rolled, and pressed fingerprints and furthermore proves to be robust to different data sources. Additionally, we introduce a novel mosaicking artifact score to quantify the severity of errors, enabling automated evaluation of fingerprint images. By addressing the challenges of mosaicking artifact detection, our work contributes to improving the accuracy and reliability of fingerprint-based biometric systems.

本研究解决了指纹拼贴过程中产生的伪影检测问题，提出了一种基于自监督学习的深度学习方法，该方法可以在没有手动标注的情况下利用大规模未标记的指纹数据进行训练。研究结果表明，该模型在多种指纹类型上准确识别拼贴错误，并引入了一种新的伪影评分机制，以量化错误的严重性，从而提高指纹生物识别系统的准确性和可靠性。

指纹拼贴伪影检测：一种自监督深度学习方法