Facial recognition technology in law enforcement: Regulating data analysis of another kind

Facial recognition technology (FRT) has emerged as a powerful tool for law enforcement, enabling the automated identification of individuals based on their unique facial features. Authorities have more and more made use of the technology to enhance criminal investigations through the analysis of images and video footage. In view of its increased use in Europe, this paper explores the legal implications of FRT in law enforcement under EU law and evaluates approaches to regulation. FRT use constitutes biometric data processing and comes with a particularly sensitive analysis of data. Its specific nature is grounded in the creation of a new (biometric) quality of data in order to subsequently compare for matches. Due to its impact on fundamental rights, this approach differs from conventional forensic analyses and must be appropriately regulated. Such regulation should consider the multiple data processing steps and reflect each step's impact on fundamental rights. From this procedural stance, the shortcomings of the EU Artificial Intelligence Act (AI Act) become evident. The AI Act contains specific rules for biometric AI systems but does not provide the necessary legal bases to justify FRT use by law enforcement. Without a comprehensive legal framework, such use is not permitted. This article provides concrete guidelines for addressing such regulation.

Authors

Institutions

Publication Details

Journal
Computer law & security review
Published
2024-11-29
DOI
https://doi.org/10.1016/j.clsr.2024.106092
Citations
5
Primary Topic
Digital Transformation in Law
Type
article
Field-Weighted Citation Impact
4.14
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Facial recognition technology in law enforcement: Regulating data analysis of another kind

Monika Simmler, Giulia Canova
5 citations
Computer law & security review
Digital Transformation in Law
4.14
article

Facial recognition technology in law enforcement: Regulating data analysis of another kind

Monika Simmler, Giulia Canova
article en
5 citations

Abstract

Facial recognition technology (FRT) has emerged as a powerful tool for law enforcement, enabling the automated identification of individuals based on their unique facial features. Authorities have more and more made use of the technology to enhance criminal investigations through the analysis of images and video footage. In view of its increased use in Europe, this paper explores the legal implications of FRT in law enforcement under EU law and evaluates approaches to regulation. FRT use constitutes biometric data processing and comes with a particularly sensitive analysis of data. Its specific nature is grounded in the creation of a new (biometric) quality of data in order to subsequently compare for matches. Due to its impact on fundamental rights, this approach differs from conventional forensic analyses and must be appropriately regulated. Such regulation should consider the multiple data processing steps and reflect each step's impact on fundamental rights. From this procedural stance, the shortcomings of the EU Artificial Intelligence Act (AI Act) become evident. The AI Act contains specific rules for biometric AI systems but does not provide the necessary legal bases to justify FRT use by law enforcement. Without a comprehensive legal framework, such use is not permitted. This article provides concrete guidelines for addressing such regulation.

Computer law & security reviewVol. 56
University of St.Gallen (CH)
Openalex Percentile: Top 5%
Digital Transformation in Law
4.14
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.