Paper substrate classification based on 3D surface micro-geometry

Malekmohamadi, Hossein and Emrith, Khemraj and Pollard, Stephen and Adams, Guy and Smith, Melvyn and Simske, Steve (2014) Paper substrate classification based on 3D surface micro-geometry. In: VISAPP 2014: 9th International Conference on Computer Vision Theory and Applications, 5-8 January 2014, Lisbon, Portugal.

Full content URL: http://visapp.visigrapp.org/Abstracts/2014/VISAPP_...

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

This paper presents an approach to derive a novel 3D signature based on the micro-geometry of paper surfaces so as to uniquely characterise and classify different paper substrates. This procedure is extremely important to confront different conducts of tampering valuable documents. We use a 4-light source photometric stereo (PS) method to recover dense 3D geometry of paper surfaces captured using an ultra-high resolution sensing device. We derived a unique signature for each paper type based on the shape index (SI) map generated from the surface normals of the 3D data. We show that the proposed signature can robustly and accurately classify paper substrates with different physical properties and different surface textures. Additionally, we present results demonstrating that our classification model using the 3D signature performs significantly better as compared to the use of conventional 2D image based descriptors extracted from both printed and non-printed paper surfaces. Accuracy of the proposed method is validated over a dataset comprising of 21 printed and 22 non-printed paper types and a measure of classification success of over 92%is achieved in both cases (92.5% for printed surfaces and 96% for the non-printed ones).

Keywords:Paper substrate classification
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
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ID Code:12758
Deposited On:21 Dec 2013 13:13

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