Using Synthetic Data to Improve Facial Expression Analysis with 3D Convolutional Networks
Abstract
Over the past few years, neural networks have made a huge improvement in object recognition and event analysis. However, due to a lack of available data, neural networks were not efficiently applied in expression analysis. In this paper, we tackle the problem of facial expression analysis using deep neural network by generating a realistic large scale synthetic labeled dataset. We train a deep 3-dimensional convolutional network on the generated dataset and empirically show how the presented method can efficiently classify facial expressions. Our method addresses four fundamental issues: (i) generating a large scale facial expression dataset that is realistic and accurate, (ii) a rich spatial representation of expressions, (iii) better spatiotemporal feature learning compared to recent techniques and (iv) with a simple linear classifier our learned features outperform state-of-the-art methods.
Cite
Text
Abbasnejad et al. "Using Synthetic Data to Improve Facial Expression Analysis with 3D Convolutional Networks." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.189Markdown
[Abbasnejad et al. "Using Synthetic Data to Improve Facial Expression Analysis with 3D Convolutional Networks." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/abbasnejad2017iccvw-using/) doi:10.1109/ICCVW.2017.189BibTeX
@inproceedings{abbasnejad2017iccvw-using,
title = {{Using Synthetic Data to Improve Facial Expression Analysis with 3D Convolutional Networks}},
author = {Abbasnejad, Iman and Sridharan, Sridha and Tien, Dung Nguyen and Denman, Simon and Fookes, Clinton and Lucey, Simon},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {1609-1618},
doi = {10.1109/ICCVW.2017.189},
url = {https://mlanthology.org/iccvw/2017/abbasnejad2017iccvw-using/}
}