An Action Unit based Hierarchical Random Forest Model to Facial Expression Recognition

Jingying Chen, Mulan Zhang, Xianglong Xue, Ruyi Xu, Kun Zhang

2017

Abstract

Facial expression recognition is important in natural human-computer interaction, research in this direction has made great progress. However, recognition in noisy environments still remains challenging. To improve the efficiency and accuracy of the expression recognition in noisy environments, this paper presents a hierarchical random forest model based on facial action units (AUs). First, an AUs based feature extraction method is proposed to extract facial feature effectively; second, a hierarchical random forest model based on different AU regions is developed to recognize the expressions in a coarse-to-fine way. The experiment results show that the proposed approach has a good performance in different environments.

References

  1. Jameel, R., Singhal, A., & Bansal, A., 2015. A comparison of performance of crisp logic and probabilistic neural network for facial expression recognition. In Next Generation Computing Technologies (NGCT), 2015 1st International Conference on (pp. 841-846). IEEE.
  2. Kahraman, Y., 2016. Facial expression recognition using geometric features. In Systems, Signals and Image Processing (IWSSIP), 2016 International Conference on (pp. 1-5). IEEE.
  3. Li, Y., Wang, S., Zhao, Y., & Ji, Q., 2013. Simultaneous facial feature tracking and facial expression recognition. IEEE Transactions on Image Processing, 22(7), 2559-2573.
  4. Cao, N. T., Ton-That, A. H., & Choi, H. I., 2014. Facial expression recognition based on local binary pattern features and support vector machine. International Journal of Pattern Recognition and Artificial Intelligence, 28(06), 1456012.
  5. Liu, W., & Wang, Z., 2006. Facial expression recognition based on fusion of multiple Gabor features. In 18th International Conference on Pattern Recognition (ICPR'06) (Vol. 3, pp. 536-539). IEEE.
  6. Satiyan, M., Nagarajan, R., & Hariharan, M., 2010. Recognition of facial expression using Haar wavelet transform. Trans. Int. J. Electr. Electron. Syst. Res. JEESR Univ. Technol. Mara UiTM, 3, 91-99.
  7. Levi, G., & Hassner, T., 2015. Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 503-510). ACM.
  8. Valstar, M. F., & Pantic, M., 2012. Fully automatic recognition of the temporal phases of facial actions. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(1), 28-43.
  9. Bartlett, M. S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., & Movellan, J., 2006. Fully automatic facial action recognition in spontaneous behavior. In 7th International Conference on Automatic Face and Gesture Recognition (FGR06) (pp. 223-230). IEEE.
  10. El Meguid, M. K. A., & Levine, M. D., 2014. Fully automated recognition of spontaneous facial expressions in videos using random forest classifiers. IEEE Transactions on Affective Computing, 5(2), 141- 154.
  11. Ekman, P., & Friesen, W. V., 1978. Manual for the facial action coding system. Consulting Psychologists Press.
  12. Dantone, M., Gall, J., Fanelli, G., & Van Gool, L., 2012. Real-time facial feature detection using conditional regression forests. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 2578-2585). IEEE.
  13. Minka, T., 1999. The dirichlet-tree distribution. Paper available online at: http://www.stat.cmu.edu/minka/ papers/dirichlet/minka-dirtree. pdf.
  14. Hager, J. C., Ekman, P., & Friesen, W. V., 2002. Facial action coding system. Salt Lake City, UT: A Human Face.
  15. Valstar, M. F., & Pantic, M., 2006. Biologically vs. logic inspired encoding of facial actions and emotions in video. In 2006 IEEE International Conference on Multimedia and Expo (pp. 325-328).
  16. Breiman, L., 2001. Random forests. Machine learning, 45(1), 5-32.
  17. Kanade, T., Cohn, J. F., & Tian, Y., 2000. Comprehensive database for facial expression analysis. In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on (pp. 46-53). IEEE.
  18. Chang, C. C., & Lin, C. J., 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
  19. Mollahosseini, A., Chan, D., & Mahoor, M. H., 2016. 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.
  20. Lyons, M., Akamatsu, S., Kamachi, M., & Gyoba, J., 1998. Coding Facial Expressions with Gabor Wavelets. IEEE International Conference on Automatic Face and Gesture Recognition, 1998. Proceedings (Vol.1998, pp.200--205).
  21. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137-154.
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Paper Citation


in Harvard Style

Chen J., Zhang M., Xue X., Xu R. and Zhang K. (2017). An Action Unit based Hierarchical Random Forest Model to Facial Expression Recognition . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 753-760. DOI: 10.5220/0006274707530760


in Bibtex Style

@conference{icpram17,
author={Jingying Chen and Mulan Zhang and Xianglong Xue and Ruyi Xu and Kun Zhang},
title={An Action Unit based Hierarchical Random Forest Model to Facial Expression Recognition},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={753-760},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006274707530760},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - An Action Unit based Hierarchical Random Forest Model to Facial Expression Recognition
SN - 978-989-758-222-6
AU - Chen J.
AU - Zhang M.
AU - Xue X.
AU - Xu R.
AU - Zhang K.
PY - 2017
SP - 753
EP - 760
DO - 10.5220/0006274707530760