Latent Fingerprint In the Wild (LFIW) Database
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Latent fingerprints are among the most important and widely used evidence in crime scenes, digital forensics and law enforcement worldwide. Despite the number of advancements reported in recent works, we note that significant open issues such as independent benchmarking and lack of largescale evaluation databases for improving the algorithms are inadequately addressed. The available databases are mostly of semi-public nature, lack of acquisition in the wild environment, and post-processing pipelines. Moreover, they do not represent a realistic capture scenario similar to real crime scenes, to benchmark the robustness of the algorithms. Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent. We introduce a new wild large-scale latent fingerprint database: Latent Fingerprint In the Wild (LFIW) that includes five different acquisition scenarios: reference fingerprints from (1) optical and (2) capacitive sensors, (3) smartphone fingerprints, latent fingerprints captured from (4) wall surface, (5) Ipad surface, and (6) aluminium foil surface. This new database consists of 600 unique fingerprint instances captured in all above-mentioned settings. A total of 1200 reference fingerprints from optical and capacitive sensors, 600 fingerphotos from smartphones, and 4200 latent fingerprints from each of the 60 subjects were provided in this work. The dataset is constructed considering various age groups, equal representations of genders and backgrounds. The LFIW latent fingerprint database enables researches in developing, testing and publishing human recognition algorithms.
潜指纹（latent fingerprints）是全球犯罪现场、数字取证与执法领域中最为重要且应用最为广泛的证据之一。尽管近期相关研究已取得诸多进展，但我们注意到，独立基准测试、缺乏用于算法优化的大规模评估数据库等关键未决问题仍未得到充分解决。现有数据库大多属于半公开性质，且缺乏野外环境下的采集流程与后处理流程。此外，此类数据库无法模拟与真实犯罪场景相近的实际采集场景，难以对算法的鲁棒性开展基准测试。此外，现有潜指纹识别数据库要么样本量不足，无法覆盖足够多的独立主体/指纹实例，要么无法提供可用于与潜指纹进行交叉比对的真实参考指纹图像。本研究提出一款全新的大规模野外潜指纹数据库——野外潜指纹数据库（Latent Fingerprint In the Wild，简称LFIW），其涵盖五种不同的采集场景：分别为（1）光学传感器、（2）电容传感器采集的参考指纹，（3）智能手机采集的指纹，以及从（4）墙面、（5）iPad表面、（6）铝箔表面采集的潜指纹。该新数据库包含上述所有采集场景下的600组独立指纹样本。本研究共提供了1200组来自光学与电容传感器的参考指纹、600组智能手机采集的指部照片，以及60名受试者各自的4200组潜指纹。该数据集的构建兼顾了不同年龄段人群，且性别与背景分布均衡。这款LFIW潜指纹数据库将助力研究人员开展人类身份识别算法的开发、测试与相关成果发表。
