The proposal is inspired by the idea G on RedHen's GSoC 2016 idea page. The main purpose is to develop models and code helping domain experts to research on art history. It includes five main tasks:

  1. Training a classifier for recognizing a limited number of known visual symbols in images of religious artworks. VGG-net based fully convolutional network models will be trained for this task. Multi-scale detection, non-maximum suppression and bounding box regression will be implemented.
  2. Training a detector for localizing person heads and hands in images of art with various styles. CNN models will be trained based on a state-of-art head detection method.
  3. Building a module identifying a few certain hand gestures in images of artworks based on task 2. With the cropped detection result of task 2, a classifier based on VGG-net will be built.
  4. Developing a tool generating a frontal view of a face given a artistic portrait with a side view of the face based on existing face frontalization code.
  5. Developing initial research code for analysis of color style changes over time of Christian paintings by machine learning.



Xi-Jin Zhang (mfs6174)


  • Line Cecilie Engh
  • Mark Turner
  • Francis Steen