Share Email Print
cover

Proceedings Paper

Printing artificial sweat using ink jet printers for the test set generation in forensics: an image quality assessment of the reproducibility of the printing results
Author(s): Mario Hildebrandt; Jennifer Sturm; Jana Dittmann
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In order to use scientific expert evidence in court hearings, several criteria must be met. In the US jurisdiction the Daubert decision2 has defined several criteria that might be assessed if a testimony is challenged. In particular the potential for testing or actual testing, as well as known or potential error rate are two very important criteria. In order to be able to compare the results with each other, the reproducible creation of evaluation samples is necessary. However, each latent fingerprint is unique due to external inuence factors such as sweat composition or pressure during the application of a trace. Hence, Schwarz1 introduces a method to print latent fingerprints using ink jet printers equipped with artificial sweat. In this paper we assess the image quality in terms of reproducibility and clarity of the printed artificial sweat patterns. For that, we determine the intra class variance from one printer on the same and on different substrates based on a subjective assessment, as well as the inter class variance between different printers of the same model using pattern recognition techniques. Our results indicate that the intra class variance is primarily inuenced by the drying behavior of the amino acid. The inter class is surprisingly large between identical models of one printer. Our evaluation is performed using 100 samples on an overhead foil and 50 samples on a compact disk surface with 5 different patterns (two line structures, a fingerprint image and two di_erent arrows with a larger area with amino acid) acquired with a Keyence VK-X110 laser scanning confocal microscope.11 The results show a significant difference between the two identical printers allowing for differentiating between them with an accuracy of up to 99%.

Paper Details

Date Published: 4 February 2013
PDF: 10 pages
Proc. SPIE 8653, Image Quality and System Performance X, 86530O (4 February 2013); doi: 10.1117/12.2004526
Show Author Affiliations
Mario Hildebrandt, Otto-von-Guericke Univ. of Magdeburg (Germany)
Jennifer Sturm, Otto-von-Guericke Univ. of Magdeburg (Germany)
Jana Dittmann, Otto-von-Guericke Univ. of Magdeburg (Germany)


Published in SPIE Proceedings Vol. 8653:
Image Quality and System Performance X
Peter D. Burns; Sophie Triantaphillidou, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray