Paper Substrate Classification based on 3D Surface Micro-geometry

Hossein Malekmohamadi; Khemraj Emrith; Stephen Pollard; Guy Adams; Melvyn Smith; Steve Simske

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Paper

Paper Substrate Classification based on 3D Surface Micro-geometry

Topics: Document Imaging in Business; Features Extraction; Image Formation, Acquisition Devices and Sensors; Machine Learning Technologies for Vision; Multimedia Forensics

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, 448-453, 2014 , Lisbon, Portugal

Authors: Hossein Malekmohamadi ¹ ; Khemraj Emrith ² ; Stephen Pollard ¹ ; Guy Adams ¹ ; Melvyn Smith ² and Steve Simske ¹

Affiliations: ¹ Hewlett Packard Laboratories, United Kingdom ; ² University of the West of England, United Kingdom

Keyword(s): Photometric Stereo, Shape index, Co-occurrence Matrices, Paper Classification.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Document Imaging in Business ; Features Extraction ; Image and Video Analysis ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors ; Multimedia Forensics

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). (More)

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Paper citation in several formats:

Malekmohamadi, H.; Emrith, K.; Pollard, S.; Adams, G.; Smith, M. and Simske, S. (2014). Paper Substrate Classification based on 3D Surface Micro-geometry. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP; ISBN 978-989-758-004-8; ISSN 2184-4321, SciTePress, pages 448-453. DOI: 10.5220/0004690904480453

@conference{visapp14,
author={Hossein Malekmohamadi. and Khemraj Emrith. and Stephen Pollard. and Guy Adams. and Melvyn Smith. and Steve Simske.},
title={Paper Substrate Classification based on 3D Surface Micro-geometry},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP},
year={2014},
pages={448-453},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004690904480453},
isbn={978-989-758-004-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP
TI - Paper Substrate Classification based on 3D Surface Micro-geometry
SN - 978-989-758-004-8
IS - 2184-4321
AU - Malekmohamadi, H.
AU - Emrith, K.
AU - Pollard, S.
AU - Adams, G.
AU - Smith, M.
AU - Simske, S.
PY - 2014
SP - 448
EP - 453
DO - 10.5220/0004690904480453
PB - SciTePress