five

NIST Special Database 301 Nail to Nail (N2N) Fingerprint Challenge Dry Run

收藏
DataCite Commons2024-04-24 更新2024-07-13 收录
下载链接:
https://data.nist.gov/od/id/6F1714704711023AE053245706818C1A1936
下载链接
链接失效反馈
官方服务:
资源简介:
In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection event held in September 2017 would be successful. To this end, biometric data from unhabituated individuals needed to be collected. That data is now released by NIST as Special Database 301.\n\nIn total, 14 fingerprint sensors were deployed during the data collection, amassing a series of rolled and plain images. The devices include rolled fingerprints captured by skilled experts from the Federal Bureau of Investigation (FBI) Biometric Training Team. Captures of slaps, palms, and other plain impression fingerprint impressions were additionally recorded. \n\nNIST also partnered with the FBI and Schwarz Forensic Enterprises to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work.

2017年4月,美国情报高级研究计划局(Intelligence Advanced Research Projects Activity,IARPA)为其“指尖对指尖(Nail to Nail,N2N)指纹挑战赛”的数据采集环节举办预演活动,旨在保障2017年9月正式数据采集环节顺利开展。为实现该目标,本次预演需采集未惯于生物特征采集流程的个体的生物特征数据。上述数据集现已由美国国家标准与技术研究院(National Institute of Standards and Technology,NIST)以特种数据库301(Special Database 301)的形式对外公开发布。 本次数据采集活动共部署14台指纹传感器,累计采集了多组捺印指纹与平压指纹图像。其中,联邦调查局(Federal Bureau of Investigation,FBI)生物特征培训团队的专业技术人员完成了捺印指纹的采集工作;此外还同步记录了拍按指纹、掌纹及其他平压式指纹印痕。 美国国家标准与技术研究院(NIST)还联合联邦调查局(FBI)与施瓦茨法医企业(Schwarz Forensic Enterprises)设计了多组活动场景,使受试者能够在不同物品表面留下指纹。所选活动及配套物品,旨在适配多种潜指纹显影技术,并模拟真实执法办案场景中常见的各类物品。
提供机构:
National Institute of Standards and Technology
创建时间:
2018-07-11
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作