3D Model-Based Continuous Emotion Recognition

Hui Chen, Jiangdong Li, Fengjun Zhang, Yang Li, Hongan Wang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1836-1845

Abstract


We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications. In our method, 3D facial models are restored from 2D images, which provide crucial clues for the enhancement of robustness to overcome large changes including out-of-plane head rotations, fast head motions and partial facial occlusions. To accurately recognize the emotion, a novel random forest-based algorithm which simultaneously integrates two regressions for 3D facial tracking and continuous emotion estimation is constructed. Moreover, via the reconstructed 3D facial model, temporal information and user-independent emotion presentations are also taken into account through our image fusion process. The experimental results show that our algorithm can achieve state-of-the-art result with higher Pearson's correlation coefficient of continuous emotion recognition in real time.

Related Material


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[bibtex]
@InProceedings{Chen_2015_CVPR,
author = {Chen, Hui and Li, Jiangdong and Zhang, Fengjun and Li, Yang and Wang, Hongan},
title = {3D Model-Based Continuous Emotion Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}