Using Synthetic Data to Improve Facial Expression Analysis With 3D Convolutional Networks

Iman Abbasnejad, Sridha Sridharan, Dung Nguyen, Simon Denman, Clinton Fookes, Simon Lucey; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1609-1618

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.

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[bibtex]
@InProceedings{Abbasnejad_2017_ICCV,
author = {Abbasnejad, Iman and Sridharan, Sridha and Nguyen, Dung and Denman, Simon and Fookes, Clinton and Lucey, Simon},
title = {Using Synthetic Data to Improve Facial Expression Analysis With 3D Convolutional Networks},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}