Abstract
Convolutional neural networks (CNNs) have become effective instruments in facial expression recognition. Very good results can be achieved with deep CNNs possessing many layers and providing a good internal representation of the learned data. Due to the potentially high complexity of CNNs on the other hand they are prone to overfitting and as a result, regularization techniques are needed to improve the performance and minimize overfitting. However, it is not yet clear how these regularization techniques affect the learned representation of faces. In this paper we examine the effects of novel regularization techniques on the training and performance of CNNs and their learned features. We train a CNN using dropout, max pooling dropout, batch normalization and different combinations of these three. We show that a combination of these methods can have a big impact on the performance of a CNN, almost halving its validation error. A visualization technique is applied to the CNNs to highlight their activations for different inputs, illustrating a significant difference between a standard CNN and a regularized CNN.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Wu, H., Gu, X.: Towards dropout training for convolutional neural networks. Neural Netw. 71, 1–10 (2015)
Zeiler, M.D., Fergus, R.: Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint 1301.3557 (2013)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint 1502.03167 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)
Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression. In: Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis, pp. 94–101 (2010)
Khorrami, P., Paine, T., Huang, T.: Do deep neural networks learn facial action units when doing expression recognition? In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 19–27 (2015)
Liu, M., Li, S., Shan, S., Chen, X.: Au-aware deep networks for facial expression recognition. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6 (2013)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 818–833. Springer, Heidelberg (2014)
Barros, P., Weber, C., Wermter, S.: Emotional expression recognition with a cross-channel convolutional neural network for human-robot interaction. In: IEEE-RAS 15th International Conference on Humanoid Robots, pp. 582–587 (2015)
Liu, P., Han, S., Meng, Z., Tong, Y.: Facial expression recognition via a boosted deep belief network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1805–1812 (2014)
Wan, L., Zeiler, M., Zhang, S., Cun, Y.L., Fergus, R.: Regularization of neural networks using dropconnect. In: Proceedings of the 30th International Conference on Machine Learning (ICML 2013), pp. 1058–1066 (2013)
Acknowledgments
This work was partially supported by the CAPES Brazilian Federal Agency for the Support and Evaluation of Graduate Education (p.n.5951–13–5), the German Research Foundation DFG under project CML (TRR 169), and the Hamburg Landesforschungsförderungsprojekt.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Hinz, T., Barros, P., Wermter, S. (2016). The Effects of Regularization on Learning Facial Expressions with Convolutional Neural Networks. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-44781-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44780-3
Online ISBN: 978-3-319-44781-0
eBook Packages: Computer ScienceComputer Science (R0)