ABSTRACT
Lockpicking forensics is currently a completely manual process, which requires a lot of skill as well as training and is therefore time-consuming as well as expensive. In this paper we make a first move to transfer the most crucial part of this specific forensic process, the contactless aquisition and analysis of traces on locking pins, into the domain of digitized forensics. To do so, we introduce a new five stage processing methodology for semi- or fully-automated lockpicking forensics. Our methodology consists of: trace positioning, acquisition, detection of traces with segmentation (or region of interest determination), determination of the trace type and the determination of the used opening methods. Within this pipeline the last three stages constitute a hierarchy of pattern recognition (PR) problems. In this paper we propose a solution approach for the Trace Positioning, Contacless Acquisition and the first of the three PR problems - the detection of traces with segmentation (or region of interest determination) on which the other two are depending. To implement this segmentation, we use texture recognition with gray-level-co-occurrence matrices to blockwisely describe the texture imposed by toolmarks with adequate features. By that we are able to distinguish between regions including potential traces and regions without relevant traces. With our presented approaches for an automated contactless acquisition and trace detection, we support and improve the classical manual forensic investigation in the field of lockpicking forensics in regards of effort, objectivity and reliability. Additionally, it creates a solid base for future work dealing with trace type determination and opening method classification. We evaluate our approach with a physical test set of 15 lock pins, from locks opened with three different opening methods. On this limited test set our approach achieves True Positive Rates of up to 85% for the detection of potentially trace wielding regions. This first result, although it still leaves room for improvement, constitutes and shows a positive tendency for a seminal first step towards semi- or fully-automated lockpicking forensics.
- J. Canny. A Computational Approach to Edge Detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-8(6), nov. 1986. Google ScholarDigital Library
- datagram. Lock picking forensics, http://goo.gl/j0uuw (shortened with url-shortener). 2009.Google Scholar
- datagram. Lock picking forensics, http://www.lockpickingforensics.com. last access 01/11/2011.Google Scholar
- datagram. Lock wiki, http://www.lockwiki.com. last access 01/11/2011.Google Scholar
- G. H. Dunteman. Principal Component Analysis. Sage Publications, 1989.Google ScholarCross Ref
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. The WEKA Data Mining Software: An Update. In SIGKDD Explorations, Volume 11, Issue 1, 2009. Google ScholarDigital Library
- R. M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. In IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (6), pages 610--621, 1973.Google ScholarCross Ref
- F. T. Inc. The Development of IBIS-TRAX 3D: BulletTRAX-3D and BrassTRAX-3D. last access 01/11/2011.Google Scholar
- D. Li. Ballistics Projectile Image Analysis for Firearm Identification. In IEEE Transactions on Image Processing 15, 2006. Google ScholarDigital Library
- A. Makrushin, M. Hildebrandt, J. Dittmann, E. Clausing, R. Fischer, and C. Vielhauer. 3D Imaging for Ballistics Analysis Using Chromatic White Light Sensor. In Proceedings of SPIE 8290, 829016, pages 1443--51, 2012.Google ScholarCross Ref
- S. Murata, P. Herman, and J. R. Lakowicz. Texture Analysis of Fluorescence Lifetime Images of AT- and GC-Rich Regions in Nuclei. In J Histochem Cytochem 49, pages 1443--51, 2001.Google ScholarCross Ref
- S. Murata, P. Herman, and J. R. Lakowicz. Texture Analysis of Fluorescence Lifetime Images of Nuclear DNA with Effect of Fluorescence Resonance Energy Transfer. In Cytometry 43, pages 94--100, 2001.Google ScholarCross Ref
- H. Scharr. Optimale Operatoren in der Digitalen Bildverarbeitung. Dissertation: Ruprecht-Karls-Universität Heidelberg, 2000.Google Scholar
- S. W. Smith. The Scientist and Engineer's Guide to Digital Signal Processing. Online-Edition, last access 01/11/2011.Google Scholar
- C. Vielhauer. Biometric User Authentication for IT Security: From Fundamentals to Handwriting (Advances in Information Security). Springer US, 1st ed. 2006 (february 12, 2010) edition, 2006. Google ScholarDigital Library
- A. Viera and J. Garrett. Understanding Interobserver Agreement: The Kappa Statistic. In Fam Med 2005, 37, pages 360--363, 2005.Google Scholar
- R. F. Walker, P. Jackway, and I. D. Longstaff. Improving Co-occurrence Matrix Feature Discrimination. In Third Conference on Digital Image Computing: Techniques and Applications, 1995.Google Scholar
Index Terms
- A first approach for the contactless acquisition and automated detection of toolmarks on pins of locking cylinders using 3D confocal microscopy
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