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Fingerprints for Machines - Optical Identification of Grinding Imprints

Ralf Dragon, Tobias Mörke, Bodo Rosenhahn, Jörn Ostermann


The profile of a 10 mm wide and 3 ÎŒm 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 represen- tation. 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.

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  author = {Ralf Dragon and Tobias M\"orke and Bodo Rosenhahn and J\"orn Ostermann},
  title = {Fingerprints for Machines - Optical Identification of Grinding Imprints},
  booktitle = {DAGM},
  year = {2011},
  keywords = {}