Automatic assessment of fingermarks quality: exploration of the possible application in the context of detection and comparison with human examiners.

Details

Serval ID
serval:BIB_A2E75FDC3194
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Automatic assessment of fingermarks quality: exploration of the possible application in the context of detection and comparison with human examiners.
Journal
Journal of forensic sciences
Author(s)
Bonnaz Bérénice, De Donno Marco, Anthonioz Alexandre, Bécue Andy
ISSN
1556-4029 (Electronic)
ISSN-L
0022-1198
Publication state
Published
Issued date
08/01/2021
Peer-reviewed
Oui
Volume
66
Number
3
Pages
879-889
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
Abstract
In forensic science, particularly in the context of latent fingermarks detection, forensic scientists are often faced with the need to assess the quality of the detected fingermarks to quantitatively interpret their results and express conclusions. Today this process is mainly carried out by human examiners referring to guidelines or provided quality scales. The largest the set of fingermarks (e.g., hundreds, thousands), the longest and the most labor-intensive this task becomes. Moreover, it is difficult to guarantee a fully objective process since the subjectivity of each individual is almost impossible to avoid, especially with regards to the interpretation of the quality scale levels or when facing fingermarks detected in an inhomogeneous manner. In this paper, the possibility of automatizing the quality assessment step is explored. The choice has been made to consider the use of quality assessment algorithms currently applied in an identification context. 150 natural fingermarks from ten donors were deposited on three different supports. These marks were detected using 1,2-indanedione/zinc or cyanoacrylate fumigation depending on the support. Then, their quality was assessed by five examiners, according to the UNIL scale, and by seven algorithms (i.e., Lights Out, Latent Fingerprint Image Quality 1 and 2, Latent Quality Metric, Expected Score Likelihood Ratio, NIST Fingerprint Image Quality, MINDTCT). Spearman and Pearson correlations were calculated, and the distribution of scores for each algorithm was charted (using boxplots) against the results provided by the human examiners. The most promising results were obtained with the LQM algorithm, more specifically with the fingermark clarity metric.
Keywords
fingerprint, identification algorithm, latent quality metric, quality assessment automation, quality scale, ridge clarity
Pubmed
Web of science
Create date
01/02/2021 12:23
Last modification date
10/07/2021 6:33
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