Fingerprints for machines: characterization and optical identification of grinding imprints

Authors:
Ralf Dragon;Tobias Mörke;Bodo Rosenhahn;Jörn Ostermann
Affiliations:
Institut für Informationsverarbeitung, Leibniz Universität Hannover, Germany;Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, Germany;Institut für Informationsverarbeitung, Leibniz Universität Hannover, Germany;Institut für Informationsverarbeitung
Venue:
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Year:
2011

Citing 8
Cited 0

Depth from focus

Pattern Recognition Letters
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Communications of the ACM
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Proceedings of the international conference on Multimedia
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IEEE Transactions on Information Theory

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Abstract

The profile of a 10mm wide and 1µm deep grinding imprint is as unique as a human fingerprint. To utilize this for fingerprinting mechanical components, a robust and strong characterization has to be used. We propose a feature-based approach, in which features of a 1D profile are detected and described in its 2D space-frequency representation. We show that the approach is robust on depth maps as well as intensity images of grinding imprints. To estimate the probability of misclassification, we derive a model and learn its parameters. With this model we demonstrate that our characterization has a false positive rate of approximately 10-20 which is as strong as a human fingerprint.