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FORMS – ForensicMarks Search∗
Manuel Keglevic and Robert Sablatnig1
Abstract—The goal of the project FORMS is to support
the search and comparison of toolmarks by forensic experts
with a semi-automatic system in order to identify and solve
connected criminal cases. The proposed methodology uses a
neural network with triplet architecture to compute similari-
ties between toolmark images. Further, to allow an accurate
evaluation under real-world conditions a dataset consisting of
more than 3000 images of cylinder locks with toolmarks from
real criminal cases is created as part of the project.
I. INTRODUCTION
Lock snapping is a common way for forced entry in
Europe. The unique imprints of the pliers used for these
break-ins significantly support the investigation of such of-
fenses and are crucial as evidence in the following court
cases. However, manual examination of these toolmarks in
order to find multiple uses of the same tool is a time
consuming task due to the amount of samples. Therefore,
the goal of the project FORMS (Forensic Marks Search) is
a two-fold solution for this problem: firstly, an application
which allows for search and comparison of toolmark images
stored in a centralized database. Secondly, a methodology
based on state-of-the-art machine learning techniques for an
automatic by similarity in order to reduce the amount of
images requiring manual examination.
The project started in Fall 2015 and is funded by the
Austrian Security Research Programme KIRAS. The project
partners are the Computer Vision Lab of the TU Wien, the
Bundeskriminalamt (Criminal Intelligence Service Austria),
the CogVis GmbH, and VICESSE.
II. TOOLMARK DATASET
Since the validity of comparative forensic examination of
toolmarks has been challenged in court, various papers have
been published on the comparison of toolmark images [5].
This lead to the development of methodologies for the
automatic comparison of striated toolmarks and datasets like
the NFI Toolmark Dataset published by Baiker et al. [1].
However, in contrast to forensic images of toolmarks from
real criminal cases, these toolmarks were created in constrai-
ned environments. Therefore, to allow an evaluation of the
real-world performance of toolmark comparison methods,
a new dataset was created as part of the FORMS project.
This dataset, created by photographing cylinder locks seized
duringcriminal investigationsusingamicroscope,consistsof
approximately 3000 toolmark images from about 50 different
*This work has been funded by the Austrian security research programme
KIRAS of the Federal Ministry for Transport, Innovation and Technology
(bmvit) under Grant 850193.
1Manuel Keglevic and Robert Sablatnig are with the Computer Vision
Lab, TU Wien,mkeglevic@caa.tuwien.ac.at Fig. 1: Image of a broken lock cylinder with toolmarks
created by a locking-plier (left). Matching toolmarks on two
lock cylinders photographed using a comparison microscope
with a magnification factor of 20 (right).
crime series. In order to investigate the influence of lighting
each of the 154 cylinder was photographed on both sides
under 11 different lighting conditions. In Figure 1 a broken
lock cylinder with toolmarks is shown on the left side.
The appearance of the toolmarks can vary heavily due to
material differences in the material, the force applied or the
lighting conditions. For example in Figure 1 on the right the
appearance difference due to varying depth of the toolmarks
is illustrated.
III. METHODOLOGY
As shown in Figure 1 on the right side, extracting fo-
reground (the toolmark) from background (lock cylinder) is
challenging due to varying background structure depth of the
toolmark. Therefore, the region of interest is marked by the
forensic expert by hand in a first step. Local image patches
extracted in these regions of interests are then compared
using a neural network. The network architecture used is
based on triplet learning which has for instance been applied
to face detection [4] and local image patches [2]. Further,
Keglevic and Sablatnig showed [3] that it can be used
to compute similarity measures for striated toolmarks. To
capture the unique properties of this problem like varying
lighting conditions and background the neural network is
trained from scratch. In order to create the necessary training
dataa ground-truth toolwas created asa plugin for the image
viewer nomacs2. This tool allows the definition and pixel
perfect alignment of matching polygons in toolmark images.
Using these annotations matching patches for the training
and evaluation process can be created along these matching
polygons. First results show promising result, however for
an in-depth assessment of the performance an evaluation has
to be performed as soon as the whole dataset is annotated.
2https://github.com/nomacs/nomacs-plugins
111
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Title
- Proceedings of the OAGM&ARW Joint Workshop
- Subtitle
- Vision, Automation and Robotics
- Authors
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas Müller
- Bernhard Blaschitz
- Svorad Stolc
- Publisher
- Verlag der Technischen Universität Graz
- Location
- Wien
- Date
- 2017
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Keywords
- Tagungsband
- Categories
- International
- Tagungsbände