Learning Templates for Artistic Portrait
Lighting Analysis

Xiaowu Chen, Xin Jin, Hongyu Wu, and Qinping Zhao


   Lighting is a key factor in creating impressive artistic portraits. In this paper, we propose to analyse portrait lighting by learning templates of lighting styles. Inspired by the experience of artists, we first define several novel features which describe the local contrasts in various face regions. The most informative features are then selected with a stepwise feature pursuit algorithm to derive the templates of various lighting styles. After that, the matching scores which measure the similarity between a testing portrait and those templates are calculated for lighting style classification. Furthermore, we train a regression model by the subjective scores and the feature responses of a template to predict the score of a portrait lighting quality. Based on the templates, a novel Face Illumination Descriptor (FID) is defined to measure the difference between two portrait lightings. Experimental results show that the learned templates can well describe the lighting styles, while the proposed approach can assess the lighting quality of artistic portraits as human being does.



Xiaowu Chen, Xin Jin, Hongyu Wu, and Qinping Zhao.Learning Templates for Artistic Portrait Lighting Analysis, IEEE Transations on Image Processing-submission,  2014     PDF

Xin Jin, Mingtian Zhao,Xiaowu Chen,Qinping Zhao, and Song-Chun Zhu.Learning Artistic Lighting Template from Portrait Photographs.   PDF