用自监督学习自动检测指纹拼接缺陷，提升生物识别系统可靠性
Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach
- 基于自监督学习，无需人工标注即可训练模型
- 在接触式、非接触式等多类指纹上均实现高精度检测
- 提出新评分机制量化拼接错误严重程度，适合质检场景
指纹拼接是将多张指纹图像合成一张主指纹的关键步骤，广泛应用于现代生物识别系统。然而该过程易产生影响图像质量的拼接误差。本文提出一种基于深度学习的新型自监督方法，用于检测并量化指纹拼接中的伪影。该方法利用大规模未标注指纹数据进行训练，无需人工标注。模型在接触式、滚动式及按压式等多种指纹模态下表现优异，且对不同数据源具有强鲁棒性。此外，本文引入一种新的拼接伪影评分机制，可定量评估图像中错误的严重程度，支持自动化质量评估。本研究有助于提升指纹生物识别系统的准确性和可靠性。
原文摘要 · Abstract (English)
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.
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