Forensic Study of Paintings Through the Comparison of Fabrics

TL;DR

This paper introduces a deep learning-based method for comparing canvas fabrics in artworks, enabling authentication and attribution without traditional thread density analysis, and demonstrates its effectiveness on museum collections.

Contribution

A novel Siamese neural network approach for textile similarity assessment in paintings, bypassing the limitations of traditional thread density map matching.

Findings

Effective comparison of canvases without thread density maps

High accuracy in textile similarity estimation

Application to museum artworks confirms method's utility

Abstract

The study of canvas fabrics in works of art is a crucial tool for authentication, attribution and conservation. Traditional methods are based on thread density map matching, which cannot be applied when canvases do not come from contiguous positions on a roll. This paper presents a novel approach based on deep learning to assess the similarity of textiles. We introduce an automatic tool that evaluates the similarity between canvases without relying on thread density maps. A Siamese deep learning model is designed and trained to compare pairs of images by exploiting the feature representations learned from the scans. In addition, a similarity estimation method is proposed, aggregating predictions from multiple pairs of cloth samples to provide a robust similarity score. Our approach is applied to canvases from the Museo Nacional del Prado, corroborating the hypothesis that plain weave canvases, widely used in painting, can be effectively compared even when their thread densities are similar. The results demonstrate the feasibility and accuracy of the proposed method, opening new avenues for the analysis of masterpieces.