19 February 2014 Digitized locksmith forensics: automated detection and segmentation of toolmarks on highly structured surfaces
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Abstract
Locksmith forensics is an important area in crime scene forensics. Due to new optical, contactless, nanometer range sensing technology, such traces can be captured, digitized and analyzed more easily allowing a complete digital forensic investigation. In this paper we present a significantly improved approach for the detection and segmentation of toolmarks on surfaces of locking cylinder components (using the example of the locking cylinder component ’key pin’) acquired by a 3D Confocal Laser Scanning Microscope. This improved approach is based on our prior work1 using a block-based classification approach with textural features. In this prior work1 we achieve a solid detection rate of 75-85% for the detection of toolmarks originating from illegal opening methods. Here, in this paper we improve, expand and fuse this prior approach with additional features from acquired surface topography data, color data and an image processing approach using adapted Gabor filters. In particular we are able of raising the detection and segmentation rates above 90% with our test set of 20 key pins with approximately 700 single toolmark traces of four different opening methods. We can provide a precise pixel- based segmentation as opposed to the rather imprecise segmentation of our prior block-based approach and as the use of the two additional data types (color and especially topography) require a specific pre-processing, we furthermore propose an adequate approach for this purpose.
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Eric Clausing, Claus Vielhauer, "Digitized locksmith forensics: automated detection and segmentation of toolmarks on highly structured surfaces", Proc. SPIE 9028, Media Watermarking, Security, and Forensics 2014, 90280W (19 February 2014); doi: 10.1117/12.2036945; https://doi.org/10.1117/12.2036945
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