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，主要源于潜指纹的低质量。本研究提出一种基于深度学习的密集细节特征描述子（DMD）用于潜指纹匹配。DMD通过提取以其中心细节特征对齐的指纹图像块获得，同时捕获精细的细节特征信息与纹理信息。我们的密集描述子采用三维表示形式，其中两个维度关联于原始图像平面，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，确保描述子仅在前景区域有效。两个描述子之间的匹配在其重叠区域进行，并采用分数归一化策略以减少有效区域外差异带来的影响。我们的描述子在多个潜指纹数据集上取得了最先进的性能。总体而言，与现有方法相比，我们的DMD具有更强的表征能力与可解释性。