Authors:
Christian Arndt
1
;
Jana Dittmann
2
and
Claus Vielhauer
3
Affiliations:
1
Otto-von-Guericke University Magdeburg, Germany
;
2
Otto-von-Guericke University Magdeburg and University of Buckingham, Germany
;
3
Otto-von-Guericke University Magdeburg and Brandenburg University of Applied Sciences, Germany
Keyword(s):
Digitized Crime Scene Forensics, Fiber Analysis, Identification, Individualization, Feature Space Evaluation.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
;
Segmentation and Grouping
Abstract:
Despite of ongoing improvements in the field of digitized crime scene forensics, a lot of analysis work is still done manually by trained experts. In this paper, we derive and define a 2048 dimensional fiber feature space from a spectral scan with a wavelength range of 163 - 844 nm sampled with FRT thin film reflectometer (FTR). Furthermore, we perform an evaluation of seven commonly used classifiers (Naive Bayes, SMO, IBk, Bagging, Rotation Forest, JRip, J48) in combination with a proven concept from the biometric field of user authentication called Biometric Hash algorithm (BioHash). We perform our evaluation in two well-known forensic examination goals: identification - determining the broad fiber group (e.g. acrylic) and individualization - finding the concrete textile originator. Our experimental test set considers 50 different fibers, each
sampled in four scan resolutions of: 100; 50; 20; 10 μm. Overall, 800 digital samples are measured. For both examination goals we can show t
hat despite the Naive Bayes all classifiers show a positive classification tendency (80 - 99%), whereby the BioHash optimization performs best for individualization tasks.
(More)