Learning Social Relation Traits From Face Images

Zhanpeng Zhang, Ping Luo, Chen-Change Loy, Xiaoou Tang; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3631-3639


Social relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine grained and high-level relation traits can be characterised and quantified from face images in the wild. To address this challenging problem we propose a deep model that learns a rich face representation to capture gender, expression, head pose, and age-related attributes, and then performs pairwise-face reasoning for relation prediction. To learn from heterogeneous attribute sources, we formulate a new network architecture with a bridging layer to leverage the inherent correspondences among these datasets. It can also cope with missing target attribute labels. Extensive experiments show that our approach is effective for fine-grained social relation learning in images and videos.

Related Material

author = {Zhang, Zhanpeng and Luo, Ping and Loy, Chen-Change and Tang, Xiaoou},
title = {Learning Social Relation Traits From Face Images},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}