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A team of computer scientists at the Interdisciplinary Center in Israel have developed an innovative method to automatically analyze artistic portraiture, capturing a high level of detail and accuracy of the portraits as well as the artists' individual style. Focused on devising a computational method to detect facial features in artwork, or facial landmarks such as eye corners or mouth corners, the researchers have successfully extended work done in photographs of natural face images to the artistic portraiture field.
"Because there is no training data of portrait paintings that contain facial landmarks, our key idea was to create such data using what we call 'artistic augmentation,'" says Professor Ariel Shamir, lead author of the study and dean of the Efi Arazi School of Computer Science at the Interdisciplinary Center Herzliya. "We transform photographic face data to be more similar to artistic portraits and use it to train new models for neural networks that work better for artistic inputs. By using our models, we not only gained better precision on portraits, but we also did not lose much accuracy on natural faces."
Shamir - Students - Jordan - Yaniv - Yael
Shamir, along with his students Jordan Yaniv and Yael Newman, are set to present their work at SIGGRAPH 2019, held 28 July-1 August in Los Angeles. This annual gathering showcases the world's leading professionals, academics, and creative minds at the forefront of computer graphics and interactive techniques.
The key differences between artistic portraits and natural facial images are twofold: texture appearance differences and geometric differences. Past work in this research area primarily focused on capturing the appearance style of art, and not the geometric style. With artistic portraits, geometric style is imperative, note the authors of the study, but challenging to capture since every artist has their own creative, distinct style.
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