Learning artistic lighting template from portrait photographs

Authors:
Xin Jin;Mingtian Zhao;Xiaowu Chen;Qinping Zhao;Song-Chun Zhu
Affiliations:
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China;Lotus Hill Institute, Ezhou, China and Department of Statistics, University of California, Los Angeles;State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China;State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China;Lotus Hill Institute, Ezhou, China and Department of Statistics, University of California, Los Angeles
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Year:
2010

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Abstract

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.