Abstract
This work proposes using the craquelure pattern of a painting as a fingerprint to verify its authenticity against prior records. Craquelure are extracted and matched from photographs in a manner robust to illumination, scale, rotation and perspective distortion. A new crack extraction technique is introduced which uses multi-scale multi-orientation morphological processing and shape analysis in each orientation sub-band. Feature extraction – a Radon-transform based local descriptor at the crack junctions – and matching are described. Matching accuracy was 98.69 % on our database of 151 genuine unique craquelure images with simulated multiple copies of each pattern.
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Acknowledgements
The authors thank the Institute for Diagnostic Imaging Research of the University of Windsor for financial support of this research. Our special thanks go to Dr. Spike Bucklow and the Hamilton Kerr Institute of the University of Cambridge for their support in providing the craquelure images.
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Taylor, J.R.B., Baradarani, A., Maev, R.G. (2015). Art Forgery Detection via Craquelure Pattern Matching. In: Garain, U., Shafait, F. (eds) Computational Forensics. IWCF IWCF 2012 2014. Lecture Notes in Computer Science(), vol 8915. Springer, Cham. https://doi.org/10.1007/978-3-319-20125-2_15
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DOI: https://doi.org/10.1007/978-3-319-20125-2_15
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