NIST SD27

Latent fingerprint identification is crucial in forensic science for linking suspects to crime scenes. Latent examiners obtain unique, reliable evidence by revealing hidden prints through advanced techniques. However, latent fingerprints often are partial prints with undesirable characteristics such as noise or distortion. Due to these characteristics, identifying the physical details of a latent fingerprint, known as minutiae, is a complex task. Recent publications found that there are subsets on one minutia in latent fingerprints that, when removed, increase the matching score. We have defined this type of minutia as obstructive. The importance of obstructive minutiae lies in their ability to increase the identification rate when they are identified and removed. In this work, we propose a new set of features to describe obstructive minutiae in latent fingerprints. Using this set of features, we have built datasets that describe latent fingerprints from which a subset of minutiae has been removed. Additionally, we have evaluated a set of multi-class classifiers trained with our datasets to predict if there are obstructive minutiae in a latent fingerprint. Finally, we designed two new algorithms to find and remove, in latent fingerprints, the obstructive minutia that generates the maximum increase in the matching score according to our set of classifiers. We used Cumulative Match Characteristic (CMC) curves to compare the relative change of identifying an initial latent fingerprint versus a latent fingerprint with the removed obstructive minutia that generates the maximum increase in the matching score.

潜指纹（latent fingerprint）识别在法医学中将嫌疑人与犯罪现场关联的场景中至关重要。潜指纹检验人员通过先进技术显现隐藏指纹，以此获取独特且可靠的证据。然而，潜指纹往往是带有噪声、畸变等不良特性的残缺指纹。受此类特性影响，识别潜指纹的物理细节——即细节点（minutiae）——是一项复杂任务。近期研究表明，潜指纹中的部分细节点子集在被移除后，匹配得分反而会升高。我们将此类细节点定义为阻塞性细节点。阻塞性细节点的价值在于，若能识别并移除它们，可提升潜指纹的识别率。本研究提出了一套全新的特征集，用于描述潜指纹中的阻塞性细节点。基于该特征集，我们构建了数据集，用于描述移除了部分细节点子集的潜指纹。此外，我们利用所构建的数据集训练了多分类器集，以预测潜指纹中是否存在阻塞性细节点。最后，我们设计了两种新算法，可基于该分类器集，在潜指纹中找到并移除能使匹配得分提升幅度最大的阻塞性细节点。我们采用累积匹配特征（Cumulative Match Characteristic，CMC）曲线，对比了初始潜指纹与移除了匹配得分提升幅度最大的阻塞性细节点后的潜指纹的识别性能相对变化。

搜集汇总

背景与挑战

背景概述

该数据集基于NIST SD27潜在指纹数据库，专注于研究指纹细节点对匹配分数的影响，特别是识别和移除'阻碍性细节点'以提高识别率。数据集包含通过移除细节点生成的特征集，用于训练分类器和开发优化算法，支持指纹识别领域的机器学习和法医科学研究。数据格式包括视频、CSV和文本文件，适用于人工智能和教育技术应用。