Artist, Style And Year Classification Using Face Recognition And Clustering With Convolutional Neural Networks

TL;DR

This paper explores using face recognition CNNs to cluster fine-art paintings by artist, year, and style, achieving promising accuracy and purity metrics, offering a novel approach beyond traditional classification methods.

Contribution

It introduces a face recognition based clustering approach for art classification, leveraging CNNs like FaceNet to analyze abundant facial features in paintings.

Findings

Artist clustering accuracy: 58.8%

Year clustering accuracy: 63.7%

Style clustering accuracy: 81.3%

Abstract

Artist, year and style classification of fine-art paintings are generally achieved using standard image classification methods, image segmentation, or more recently, convolutional neural networks (CNNs). This works aims to use newly developed face recognition methods such as FaceNet that use CNNs to cluster fine-art paintings using the extracted faces in the paintings, which are found abundantly. A dataset consisting of over 80,000 paintings from over 1000 artists is chosen, and three separate face recognition and clustering tasks are performed. The produced clusters are analyzed by the file names of the paintings and the clusters are named by their majority artist, year range, and style. The clusters are further analyzed and their performance metrics are calculated. The study shows promising results as the artist, year, and styles are clustered with an accuracy of 58.8, 63.7, and 81.3 percent, while the clusters have an average purity of 63.1, 72.4, and 85.9 percent.