Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication

Authors

  • Ahmed Elgammal Rutgers University, Artrendex LLC
  • Yan Kang Jd.Com American Technologies Corporation
  • Milko Den Leeuw Atelier for Restoration & Research of Paintings (ARRS),  The Hague

Keywords:

Deep Neural Networks, Art authentication,

Abstract

This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of unknown authors in a way that is not easy to be deceived by forged art. The methodology used is based on quantifying the characteristics of individual strokes in drawings. We propose a novel algorithm for segmenting individual strokes. We propose an approach that combines different hand-crafted and learned features for the task of quantifying stroke characteristics. We experimented with a dataset of 300 digitized drawings with over 80 thousands strokes. The collection mainly consisted of drawings of Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of representative works of other artists. The experiments shows that the proposed methodology can classify individual strokes with accuracy 70%-90%, and aggregate over drawings with accuracy above 80%, while being robust to be deceived by fakes.

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Published

2018-04-25

How to Cite

Elgammal, A., Kang, Y., & Den Leeuw, M. (2018). Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11313