ForschungPublikationen
Fingerprints for Machines - Characterization and Optical Identification of Grinding Imprints.

Fingerprints for Machines - Characterization and Optical Identification of Grinding Imprints.

Kategorien Konferenz (reviewed)
Jahr 2011
Autoren Dragon, R., Mörke, T., Rosenhahn, B., Ostermann, J.:
Veröffentlicht in DAGM Conference 2011, 33rd Annual Symposium of the German Association for Pattern Recognition, August 30th-September 2nd, 2011, Frankfurt am Main, 10 Seiten.
Beschreibung

The profile of a 10 mm wide and 3 µm 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 is as strong as a human fingerprint.