Automated ‘lights-out’ searching of all recovered fingerprints: Review of the current workflow for latent fingerprint processing in Queensland, Australia

指纹(计算) 工作流程 计算机科学 匹配(统计) 过程(计算) 指纹验证比赛 指纹识别 数据挖掘 人工智能 数据库 医学 病理 操作系统
作者
Troy O’Malley,Matt N. Krosch,Paul Peacock,Rechelle Cook,David H. Neville
出处
期刊:Forensic Science International [Elsevier BV]
卷期号:337: 111372-111372 被引量:3
标识
DOI:10.1016/j.forsciint.2022.111372
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
坤坤发布了新的文献求助10
1秒前
mjhk完成签到,获得积分10
1秒前
好久不见发布了新的文献求助20
2秒前
亿万斯年应助LYF000666采纳,获得20
2秒前
ZD小草完成签到 ,获得积分10
4秒前
4秒前
桐桐应助雨碎寒江采纳,获得10
4秒前
小蘑菇应助浮浮世世采纳,获得10
7秒前
7秒前
7秒前
8秒前
Owen应助sam采纳,获得10
8秒前
YDL发布了新的文献求助10
8秒前
9秒前
Acerie完成签到,获得积分10
10秒前
快乐的鱼发布了新的文献求助10
12秒前
12秒前
你女发布了新的文献求助10
13秒前
木子发布了新的文献求助10
13秒前
量子星尘发布了新的文献求助10
14秒前
pluto应助科研通管家采纳,获得10
15秒前
LaTeXer应助科研通管家采纳,获得150
15秒前
pluto应助科研通管家采纳,获得10
15秒前
llll发布了新的文献求助10
15秒前
15秒前
桐桐应助科研通管家采纳,获得10
15秒前
科研通AI6应助科研通管家采纳,获得10
15秒前
华仔应助科研通管家采纳,获得10
16秒前
桐桐应助科研通管家采纳,获得30
16秒前
pluto应助科研通管家采纳,获得10
16秒前
pluto应助科研通管家采纳,获得10
16秒前
隐形曼青应助科研通管家采纳,获得10
16秒前
LaTeXer应助科研通管家采纳,获得150
16秒前
pluto应助科研通管家采纳,获得10
16秒前
浮游应助科研通管家采纳,获得10
16秒前
pluto应助科研通管家采纳,获得10
16秒前
小蘑菇应助科研通管家采纳,获得30
16秒前
JamesPei应助科研通管家采纳,获得30
16秒前
16秒前
16秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Zeolites: From Fundamentals to Emerging Applications 1500
Architectural Corrosion and Critical Infrastructure 1000
Early Devonian echinoderms from Victoria (Rhombifera, Blastoidea and Ophiocistioidea) 1000
Hidden Generalizations Phonological Opacity in Optimality Theory 1000
Handbook of Social and Emotional Learning, Second Edition 900
2026国自然单细胞多组学大红书申报宝典 800
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4915038
求助须知:如何正确求助?哪些是违规求助? 4189167
关于积分的说明 13010035
捐赠科研通 3958176
什么是DOI,文献DOI怎么找? 2170103
邀请新用户注册赠送积分活动 1188349
关于科研通互助平台的介绍 1096077