Discerning the painter's hand: machine learning on surface topography

TL;DR

This study demonstrates that machine learning applied to surface topography can effectively attribute paintings, achieving up to 96% accuracy and outperforming color image analysis, especially at small surface scales.

Contribution

The paper introduces a novel approach using surface topography data and CNNs for painting attribution, highlighting the importance of small-scale surface features.

Findings

Surface topography analysis achieves 60-96% attribution accuracy.

Small surface features as small as twice a bristle diameter are key for attribution.

Surface topography outperforms color images in attribution accuracy.

Abstract

Attribution of paintings is a critical problem in art history. This study extends machine learning analysis to surface topography of painted works. A controlled study of positive attribution was designed with paintings produced by a class of art students. The paintings were scanned using a confocal optical profilometer to produce surface data. The surface data were divided into virtual patches and used to train an ensemble of convolutional neural networks (CNNs) for attribution. Over a range of patch sizes from 0.5 to 60 mm, the resulting attribution was found to be 60 to 96% accurate, and, when comparing regions of different color, was nearly twice as accurate as CNNs using color images of the paintings. Remarkably, short length scales, as small as twice a bristle diameter, were the key to reliably distinguishing among artists. These results show promise for real-world attribution, particularly in the case of workshop practice.