  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.
