  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.
