Predicting suitability of finger marks using machine learning techniques and examiner annotations

Details

Ressource 1Request a copy Under indefinite embargo.
UNIL restricted access
State: Public
Version: author
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_4C4B618FFB2D
Type
Article: article from journal or magazin.
Collection
Publications
Institution
UNIL/CHUV
Title
Predicting suitability of finger marks using machine learning techniques and examiner annotations
Journal
Forensic Science International
Author(s)
Eldridge Heidi, De Donno Marco, Champod Christophe
ISSN
0379-0738
Publication state
Published
Issued date
03/2021
Volume
320
Pages
110712
Language
english
Keywords
Pathology and Forensic Medicine
OAI-PMH
oai:serval.unil.ch:BIB_4C4B618FFB2D
DOI
10.1016/j.forsciint.2021.110712
Open Access
Yes
Funding(s)
University of Lausanne
Create date
17/02/2021 9:14
Last modification date
18/02/2021 6:28
Usage data