Learning Artistic Lighting Template from Portrait Photographs

Xin Jin1     Mingtian Zhao2,3     Xiaowu Chen1     Qinping Zhao1     Song-Chun Zhu2,3
1Beihang University       2Lotus Hill Institute       3University of California, Los Angeles


This paper presents a method for learning artistic portrait lighting template from a dataset of artistic and daily portrait photographs. The learned template can be used for (1) classification of artistic and daily portrait photographs, and (2) numerical aesthetic quality assessment of these photographs in lighting usage. For learning the template, we adopt Haar-like local lighting contrast features, which are then extracted from pre-defined areas on frontal faces, and selected to form a log-linear model using a stepwise feature pursuit algorithm. Our learned template corresponds well to some typical studio styles of portrait photography. With the template, the classification and assessment tasks are achieved under probability ratio test formulations. On our dataset composed of 350 artistic and 500 daily photographs, we achieve a 89.5% classification accuracy in cross-validated tests, and the assessment model assigns reasonable numerical scores based on portraits' aesthetic quality in lighting.

Paper and Poster

Paper Poster presented at ECCV 2010


  author = {Xin Jin and Mingtian Zhao and Xiaowu Chen and Qingping Zhao and Song-Chun Zhu},
  title = {Learning Artistic Lighting Template from Portrait Photographs},
  booktitle = {ECCV '10: Proceedings of the 11th European Conference on Computer Vision},
  year = {2010},
  location = {Heraklion, Crete, Greece},
  pages = {IV: 101--114},