Learning Social Relation Traits from Face Images

International Conference on Computer Vision (ICCV) 2015, Santiago, Chile

Abstract

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.

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Citation

@inproceedings{SOCIALRELATION_ICCV2015,
 author = {Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang},
 title = {Learning Social Relation Traits from Face Images},
 booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
 month = December,
 year = {2015}
} 
	
@inproceedings{SOCIALRELATION_2017,
 author = {Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang},
 title = {From Facial Expression Recognition to Interpersonal Relation Prediction},
 booktitle = {arXiv:1609.06426v2},
 month = September,
 year = {2016}
} 
    

Contact

Zhanpeng Zhang
cuhkzhanpeng@gmail.com