Past Events

PhD Defense

Application of Deep Neural Networks to Automated Analysis of Hatching Lines for Artist Identification


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Thursday, July 14, 2022, 02:00pm


Speaker: Shahrzad Ziaee

Location : Virtual


Professor Ahmed Elgammal (Chair)

Professor Casimir Kulikowski

Professor Abdeslam Boularias

Professor Lior Shamir (Kansas State University)

Event Type: PhD Defense

Abstract: Automated analysis of art has gained interest in the computer science research community over the past decade, resulting in the onset of computer vision solutions for authentication and attribution of art. Attribution and authentication are two of the most crucial tasks in the domain of art. Several studies have approached these tasks with an emphasis on analyzing paintings in the visual spectrum; however, with a limited scope to a specific artist, limited datasets, or without rigorous evaluations of the robustness of such approaches. Contrary to paintings, the application of computer vision methodologies for authentication and attribution of drawings and prints has been only sparsely explored. In this dissertation, we introduce a novel approach for attribution and authentication of drawings and sketches at the visual spectrum by automated analysis of hatching lines. Hatching is a prevalent technique in drawing and printmaking, often used to introduce toning, shading, or illusion of light and volume. Since artists tend to add hatching lines spontaneously, we hypothesize that these areas of drawings could carry unique physical and unconscious characteristics of the artist, which can be used to identify the artist. We further hypothesize that such unique characteristics can be quantified by a computational model that will enable automatic attribution and authentication. We investigated the application of deep convolutional neural networks for detecting hatching lines and identifying artists based on their hatching characteristics. We conducted several experiments on different sets of drawings and prints by different artists from different eras, using different techniques and with various degrees of complexity. We concluded that we can indeed identify artists based solely on their hatching with an accuracy of 90-100% in most cases.


Department of Computer Science

School of Arts & Sciences

Rutgers University

Contact  Professor Ahmed Elgammal

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