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2018 | OriginalPaper | Chapter

Multi-region Ensemble Convolutional Neural Network for Facial Expression Recognition

Authors : Yingruo Fan, Jacqueline C. K. Lam, Victor O. K. Li

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

Facial expressions play an important role in conveying the emotional states of human beings. Recently, deep learning approaches have been applied to image recognition field due to the discriminative power of Convolutional Neural Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble CNN (MRE-CNN) framework for facial expression recognition, which aims to enhance the learning power of CNN models by capturing both the global and the local features from multiple human face sub-regions. Second, the weighted prediction scores from each sub-network are aggregated to produce the final prediction of high accuracy. Third, we investigate the effects of different sub-regions of the whole face on facial expression recognition. Our proposed method is evaluated based on two well-known publicly available facial expression databases: AFEW 7.0 and RAF-DB, and has been shown to achieve the state-of-the-art recognition accuracy.

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Literature
1.
Dhall, A., Goecke, R., Lucey, S., Gedeon, T., et al.: Collecting large, richly annotated facial-expression databases from movies. IEEE Multimed. 19(3), 34–41 (2012)CrossRef
2.
Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Person. Soc. Psychol. 17(2), 124 (1971)CrossRef
3.
He, Z., Fan, Y., Zhuang, J., Dong, Y., Bai, H.: Correlation filters with weighted convolution responses. In: 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 1992–2000. IEEE (2017)
4.
Hu, P., Cai, D., Wang, S., Yao, A., Chen, Y.: Learning supervised scoring ensemble for emotion recognition in the wild. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 553–560. ACM (2017)
5.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
6.
Li, S., Deng, W., Du, J.: Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2584–2593. IEEE (2017)
7.
Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10. IEEE (2016)
8.
Ng, H.W., Nguyen, V.D., Vonikakis, V., Winkler, S.: Deep learning for emotion recognition on small datasets using transfer learning. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 443–449. ACM (2015)
9.
Ouyang, X., et al.: Audio-visual emotion recognition using deep transfer learning and multiple temporal models. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 577–582. ACM (2017)
10.
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef
11.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2014)
12.
Vielzeuf, V., Pateux, S., Jurie, F.: Temporal multimodal fusion for video emotion classification in the wild. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 569–576. ACM (2017)
13.
Yan, J., Zheng, W., Cui, Z., Tang, C., Zhang, T., Zong, Y.: Multi-cue fusion for emotion recognition in the wild. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 458–463. ACM (2016)
Metadata
Title
Multi-region Ensemble Convolutional Neural Network for Facial Expression Recognition
Authors
Yingruo Fan
Jacqueline C. K. Lam
Victor O. K. Li
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_9

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