ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints
This addresses the problem of similarity analysis in artworks for art historians and researchers, but it is incremental as it adapts existing methods to a specific domain with synthetic data augmentation.
The paper tackles facial landmark detection in high-resolution paintings and prints by proposing a deep-learning method that uses global and region networks for coarse and refined predictions, achieving accurate detection on a high-resolution dataset and comparable performance on a low-resolution public dataset.
Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists. With facial landmarks, portraits of different genres, such as paintings and prints, can be automatically aligned using control-point-based image registration. We propose a deep-learning-based method for facial landmark detection in high-resolution images of paintings and prints. It divides the task into a global network for coarse landmark prediction and multiple region networks for precise landmark refinement in regions of the eyes, nose, and mouth that are automatically determined based on the predicted global landmark coordinates. We created a synthetically augmented facial landmark art dataset including artistic style transfer and geometric landmark shifts. Our method demonstrates an accurate detection of the inner facial landmarks for our high-resolution dataset of artworks while being comparable for a public low-resolution artwork dataset in comparison to competing methods.