论文标题
使用卷积神经网络的面部识别和聚类的艺术家，风格和年份分类
Artist, Style And Year Classification Using Face Recognition And Clustering With Convolutional Neural Networks
论文作者
论文摘要
通常使用标准图像分类方法，图像分割或最近的卷积神经网络（CNN）来实现精美画作的艺术家，年份和样式分类。这项工作旨在使用新开发的面部识别方法，例如使用CNN的面部识别方法，使用CNN使用绘画中提取的面孔聚集精美绘画，这些绘画大量发现。选择了由1000多名艺术家的80,000幅绘画组成的数据集，并执行了三个独立的面部识别和聚类任务。生产的集群通过绘画的文件名进行分析，簇由其多数艺术家，年份和风格命名。进一步分析群集，并计算其性能指标。该研究显示出令人鼓舞的结果，因为艺术家，年份和样式的准确度为58.8％，63.7％和81.3％，而簇的平均纯度为63.1％，72.4％和85.9％。
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.
