TY - JOUR
T1 - The evaluation of fingermarks given activity level propositions
AU - de Ronde, Anouk
AU - Kokshoorn, Bas
AU - de Poot, Christianne J.
AU - de Puit, Marcel
PY - 2019
Y1 - 2019
N2 - Fingermarks are highly relevant in criminal investigations for individualization purposes. In some cases, the question in court changes from ‘Who is the source of the fingermarks?’ to ‘How did the fingermark end up on the surface?’. In this paper, we explore the evaluation of fingermarks given activity level propositions by using Bayesian networks. The variables that provide information on activity level questions for fingermarks are identified and their current state of knowledge with regards to fingermarks is discussed. We identified the variables transfer, persistency, recovery, background fingermarks, location of the fingermarks, direction of the fingermarks, the area of friction ridge skin that left the mark and pressure distortions as variables that may provide information on how a fingermark ended up on a surface. Using three case examples, we show how Bayesian networks can be used for the evaluation of fingermarks given activity level propositions.
AB - Fingermarks are highly relevant in criminal investigations for individualization purposes. In some cases, the question in court changes from ‘Who is the source of the fingermarks?’ to ‘How did the fingermark end up on the surface?’. In this paper, we explore the evaluation of fingermarks given activity level propositions by using Bayesian networks. The variables that provide information on activity level questions for fingermarks are identified and their current state of knowledge with regards to fingermarks is discussed. We identified the variables transfer, persistency, recovery, background fingermarks, location of the fingermarks, direction of the fingermarks, the area of friction ridge skin that left the mark and pressure distortions as variables that may provide information on how a fingermark ended up on a surface. Using three case examples, we show how Bayesian networks can be used for the evaluation of fingermarks given activity level propositions.
KW - Activity
KW - Bayesian network
KW - Evidence interpretation
KW - Touch traces
UR - http://www.scopus.com/inward/record.url?scp=85071284303&partnerID=8YFLogxK
U2 - 10.1016/j.forsciint.2019.109904
DO - 10.1016/j.forsciint.2019.109904
M3 - Article
AN - SCOPUS:85071284303
SN - 0379-0738
VL - 302
JO - Forensic Science International
JF - Forensic Science International
M1 - 109904
ER -
