Abstract：
The process of linking an offender to a crime scene via their fingerprints has historically required significant human effort to compare latent fingerprints recovered from the scene with known fingerprints of a suspect. Increasing the speed of such comparisons, whilst maintaining accuracy and reliability and minimising error, is crucial for providing rapid intelligence to police investigators. One major opportunity for streamlining fingerprint examination is the adaptation of \'lights-out\' technology to the comparison and matching of latent fingerprints. Here, we review the development, trial and validation process undertaken by the Queensland Police Service (QPS), Australia, to support implementation of a lights-out latent (LOL) workflow for automated latent fingerprint searching that is fully integrated with the existing case management systems. Targeted trials were undertaken using random selections of previously identified latent fingerprints that were searched using the LOL workflow against a local 10-print database. The results suggested that the LOL workflow could identify up to 44% of latent fingerprints with minimal human intervention and supported its implementation for all latent fingerprint comparisons in QPS casework. Review of LOL casework comparison outcomes for 2019 revealed that LOL-based identifications contributed approximately one quarter of all fingerprint identifications. Several procedural and technical factors that influenced the speed and efficiency of the LOL workflow are discussed, along with opportunities for improvement and future validation as an expert system.
摘要：
历史上，通过指纹将罪犯与犯罪现场联系起来的过程需要大量的人力来将从现场恢复的潜在指纹与嫌疑人的已知指纹进行比较。加快这种比较的速度，同时保持准确性和可靠性，并最大限度地减少误差，对于向警方调查人员提供快速情报至关重要。简化指纹检查的一个主要机会是“熄灭灯”技术适应潜在指纹的比较和匹配。这里,我们回顾了发展，昆士兰州警察局(QPS)进行的试验和验证过程，澳大利亚,支持实施与现有病例管理系统完全集成的自动潜在指纹搜索的熄灭(LOL)工作流程。使用先前识别的潜在指纹的随机选择进行有针对性的试验，这些指纹是使用LOL工作流程针对本地10打印数据库进行搜索的。结果表明，LOL工作流程可以在最少的人为干预下识别多达44％的潜在指纹，并支持QPS案例中所有潜在指纹比较的实施。对2019年LOL案例比较结果的审查显示，基于LOL的识别贡献了所有指纹识别的大约四分之一。讨论了影响LOL工作流程速度和效率的几个程序和技术因素，以及作为专家系统的改进和未来验证的机会。
