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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Charney, N., Denton, P., Kleberg, J.: Protecting Cultural Heritage from Art Theft (2012). http://www.fbi.gov/stats-services/publications/law-enforcement-bulletin/march-2012/. Accessed March 2014
Taylor, J.R.B., Severin, F., Baradarani, A., Maev, R.: 3D ultrasonic system for finger-print imaging. In: Military Health Systems Research Symposium, Fort Lauderdale, FL, USA (2013)
Bucklow, S.: A stylometric analysis of craquelure. Comput. Humanit. 31, 503–521 (1998)
El-Youssef, M., Bucklow, S., Maev, R.: The development of a diagnostic method for geographical and condition-based analysis of artworks using craquelure pattern recognition techniques. Insight Nondestr. Test. Condition Monit. 56(3), 124–130 (2014)
Abas, F.S., Martinez, K.: Classification of painting cracks for content-based analysis. In: Electronic Imaging 2003, pp. 149–160. International Society for Optics and Photonics (2003)
Giakoumis, I., Nikolaidis, N., Pitas, I.: Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans. Image Process. 15(1), 178–188 (2006). IEEE Press, New York
Gancarczyk, J.: Feature vector definition for a decision tree based craquelure identification in old paintings. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part I. LNCS, vol. 7583, pp. 542–550. Springer, Heidelberg (2012)
Zuiderveld, K.: Contrast Limited Adaptive Histograph Equalization. Graphic Gems IV, pp. 474–485. Academic Press Professional, San Diego (1994)
Otsu, N.V.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979). IEEE Press, New York
Arcelli, C., Di Baja, G.S.: A width-independent fast thinning algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 7(4), 463–474 (1985). IEEE Press, New York
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). Springer, Heidelberg
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Bathina, Y. B., Medathati, M. V., Sivaswamy, J.: Robust matching of multi-modal retinal images using radon transform based local descriptor. In: Proceedings of ACM International Health Informatics Symposium, vol. 1, pp 765–770. ACM, New York (2010)
Cappelli, R., Ferrara, M., Maltoni, D.: Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2128–2141 (2010). IEEE Press, New York
Kovesi, P. D.: MATLAB and Octave Functions for Computer Vision and Image Processing. School of Computer Science and Software Engineering, University of Western Australia. http://www.csse.uwa.edu.au/~pk/research/matlabfns. Accessed March 2014
Izenman, A.J.: Linear Discriminant Analysis, pp. 237–280. Springer, New York (2008)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-20125-2_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20124-5
Online ISBN: 978-3-319-20125-2
eBook Packages: Computer ScienceComputer Science (R0)