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The Visual Computer

, Volume 35, Issue 4, pp 535–548 | Cite as

Emotion information visualization through learning of 3D morphable face model

  • Hai Jin
  • Xun Wang
  • Yuanfeng Lian
  • Jing HuaEmail author
Original Article

Abstract

Analysis and visualization of human facial expressions and its applications are useful but challenging. This paper presents a novel approach to analyze the facial expressions from images through learning of a 3D morphable face model and a quantitative information visualization scheme for exploring this type of visual data. More specifically, a 3D face database with various facial expressions is employed to build a nonnegative matrix factorization (NMF) part-based morphable 3D face model. From an input image, a 3D face with expression can be reconstructed iteratively by using the NMF morphable 3D face model as a priori knowledge, from which basis parameters and a displacement map are extracted as features for facial emotion analysis and visualization. Based upon the features, two support vector regressions are trained to determine the fuzzy valence–arousal (VA) values to quantify the emotions. The continuously changing emotion status can be intuitively analyzed by visualizing the VA values in VA space. Our emotion analysis and visualization system, based on 3D NMF morphable face model, detect expressions robustly from various head poses, face sizes and lighting conditions and is fully automatic to compute the VA values from images or a sequence of video with various facial expressions. To evaluate our novel method, we test our system on publicly available databases and evaluate the emotion analysis and visualization results. We also apply our method to quantifying emotion changes during motivational interviews. These experiments and applications demonstrate the effectiveness and accuracy of our method.

Keywords

3D morphable face model Facial expression analysis Emotion visualization 

Notes

Acknowledgements

We would like to thank the reviewers for their valuable suggestions which helped to improve this paper. This work is supported in part by the following grants: LZ16F020002 and NSF CNS-1647200.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Wayne State UniversityDetroitUSA

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