Matching Score Models for Hyperspectral Range Analysis to Improve Wood Log Traceability by Fingerprint Methods

Rudolf Schraml, Karl Entacher, Alexander Petutschnigg, Timothy Young and Andreas Uhl
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Rudolf Schraml: Department of Computer Sciences, University of Salzburg, 5020 Salzburg, Austria
Karl Entacher: Holztechnikum Kuchl, 5431 Kuchl, Austria
Alexander Petutschnigg: Department of Forest Products Technology and Timber Construction, University of Applied Sciences Salzburg, 5412 Puch bei Hallein, Austria
Timothy Young: Center for Renewable Carbon, University of Tennessee, Knoxville, TN 37996, USA
Andreas Uhl: Department of Computer Sciences, University of Salzburg, 5020 Salzburg, Austria

Mathematics, 2020, vol. 8, issue 7, 1-10

Abstract: Traceability of natural resources, from the cradle to the final product is a crucial issue to secure sustainable material usage as well as to optimize and control processes over the whole supply chain. In the forest products industries the material can be tracked by different technologies, but for the first step of material flow, from the forest to the industry, no systematic and complete technology has been developed. On the way to close this data gap the fingerprint technology for wooden logs looks promising. It uses inherent properties of a wood stem for identification. In this paper hyperspectral cameras are applied to gain images of Norway spruce (Picea abies [L.] Karst.) log end faces in different spectral ranges. The images are converted to a biometric template of feature vectors and a matching algorithm is used to evaluate if the biometric templates are similar or not. Based on this, matching scores specific spectral ranges which contain information to distinguish between different log end faces are identified. The method developed in this paper is a necessary and successful step to define scanning system parameters for fingerprint recognition systems for wood log traceability from the forest.

Keywords: wood traceability; biometric identification; fingerprint detection; hyperspectral imaging (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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