Fingerprints for machines - Characterization and optical identification of grinding imprints

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. © 2011 Springer-Verlag.