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Spectral Fiber Feature Space Evaluation for Crime Scene Forensics - Traditional Feature Classification vs. BioHash Optimization

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 that despite the Naive Bayes all classifiers show a positive classification tendency (80 99%), whereby the BioHash optimization performs best for individualization tasks.