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

arXiv: https://arxiv.org/abs/2106.07134 · HTML: https://mlsanity.com/paper/2106.07134/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)

## Built on by (most cited first, 5 of 5)

- [PATCH: a deep learning method to assess heterogeneity of artistic practice in historical paintings](https://mlsanity.com/paper/2502.01912/) (2025)
- [Disruptive Transformation of Artworks in Master-Disciple Relationships: The Case of Ukiyo-e Artworks](https://mlsanity.com/paper/2505.08284/) (2025)
- [Patch-Based Spatial Authorship Attribution in Human-Robot Collaborative Paintings](https://mlsanity.com/paper/2602.17030/) (2026)
- [Enhancing Authorship Attribution with Synthetic Paintings](https://mlsanity.com/paper/2603.04343/) (2026)
- [ArtMine: Discovering and Formalizing Artistic Processes](https://mlsanity.com/paper/2607.08331/) (2026)

## Abstract (arXiv)

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.