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This paper proposes a computational approach for analysis of strokes in linedrawings by artists. We aim at developing an AI methodology that facilitatesattribution of drawings of unknown authors in a way that is not easy to bedeceived by forged art. The methodology used is based on quantifying thecharacteristics of individual strokes in drawings. We propose a novel algorithmfor segmenting individual strokes. We designed and compared differenthand-crafted and learned features for the task of quantifying strokecharacteristics. We also propose and compare different classification methodsat the drawing level. We experimented with a dataset of 300 digitized drawingswith over 80 thousands strokes. The collection mainly consisted of drawings ofPablo Picasso, Henry Matisse, and Egon Schiele, besides a small number ofrepresentative works of other artists. The experiments shows that the proposedmethodology can classify individual strokes with accuracy 70%-90%, andaggregate over drawings with accuracy above 80%, while being robust to bedeceived by fakes (with accuracy 100% for detecting fakes in most settings).

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