Abstract Fingerprint image enhancement is one of the fundamental modules in an automated fingerprint recognition system (AFRS). While the performance of AFRS advances with sophisticated fingerprint matching algorithms, poor fingerprint image quality remains a major issue to achieve accurate fingerprint recognition. In this paper, we present a multi-task convolutional neural network (CNN) based method to recover fingerprint ridge structures from corrupted fingerprint images. By learning from the noises and corruptions caused by various undesirable conditions of finger and sensor, the proposed CNN model consists of two streams that reconstruct the fingerprint image and orientation field simultaneously. The enhanced fingerprint is further refined using the orientation field information. Moreover, we create a deliberately corrupted fingerprint image dataset associated with ground truth images to facilitate the supervised learning of the proposed CNN model. Experimental results show significant improvement on both image quality and fingerprint matching accuracy after applying the proposed fingerprint image enhancement technique to several well-known fingerprint datasets.

中文翻译：

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用于恢复损坏的指纹图像的多任务 CNN

摘要 指纹图像增强是自动指纹识别系统（AFRS）的基本模块之一。虽然 AFRS 的性能随着复杂的指纹匹配算法而提高，但指纹图像质量差仍然是实现准确指纹识别的主要问题。在本文中，我们提出了一种基于多任务卷积神经网络 (CNN) 的方法来从损坏的指纹图像中恢复指纹脊结构。通过从手指和传感器的各种不良条件引起的噪声和损坏中学习，所提出的 CNN 模型由两个流组成，它们同时重建指纹图像和方向场。使用方向场信息进一步细化增强后的指纹。而且，我们创建了一个与地面实况图像相关的故意损坏的指纹图像数据集，以促进所提出的 CNN 模型的监督学习。实验结果表明，将所提出的指纹图像增强技术应用于几个众所周知的指纹数据集后，图像质量和指纹匹配精度都有显着提高。