Latent fingerprint matching is a daunting task, primarily due to the poor quality of latent fingerprints. In this study, we propose a deep-learning based dense minutia descriptor (DMD) for latent fingerprint matching. A DMD is obtained by extracting the fingerprint patch aligned by its central minutia, capturing detailed minutia information and texture information. Our dense descriptor takes the form of a three-dimensional representation, with two dimensions associated with the original image plane and the other dimension representing the abstract features. Additionally, the extraction process outputs the fingerprint segmentation map, ensuring that the descriptor is only valid in the foreground region. The matching between two descriptors occurs in their overlapping regions, with a score normalization strategy to reduce the impact brought by the differences outside the valid area. Our descriptor achieves state-of-the-art performance on several latent fingerprint datasets. Overall, our DMD is more representative and interpretable compared to previous methods.

翻译：潜指纹匹配是一项艰巨的任务，主要由于潜指纹质量较差。本研究提出一种基于深度学习的密集细节点描述符（DMD），用于潜指纹匹配。DMD通过提取以其中心细节点对齐的指纹图像块获得，能够捕获详细的细节点信息和纹理信息。该密集描述符采用三维表示形式，其中两个维度对应原始图像平面，另一维度表示抽象特征。此外，提取过程同时输出指纹分割图，确保描述符仅在前景区域有效。两个描述符之间的匹配在其重叠区域进行，并采用分数归一化策略减少有效区域外部差异带来的影响。本描述符在多个潜指纹数据集上取得了最先进的性能。总体而言，与先前方法相比，本DMD更具代表性和可解释性。