Abstract
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.
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Acknowledgement
Thanks to Daniel Hess, Oliver Mack, Daniel Görres, Wibke Ottweiler, GNM, and Gunnar Heydenreich, CDA, and Thomas Klinke, TH Köln, and Amalie Hänsch, FAU Erlangen-Nürnberg for providing image data, and to Leibniz Society for funding the research project “Critical Catalogue of Luther portraits (1519–1530)” with grant agreement No. SAW-2018-GNM-3-KKLB, to the European Union’s Horizon 2020 research and innovation programme within the Odeuropa project under grant agreement No. 101004469 for funding this publication, and to NVIDIA for their GPU hardware donation.
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Sindel, A., Maier, A., Christlein, V. (2023). ArtFacePoints: High-Resolution Facial Landmark Detection in Paintings and Prints. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13801. Springer, Cham. https://doi.org/10.1007/978-3-031-25056-9_20
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