Abstract
Facial Expression Recognition (FER) is increasingly gaining importance in various emerging affective computing applications. In this article, we propose a Facial Expression Recognition (FER) method, based on kernel enhanced Convolutional Neural Network (CNN) model. Our method improves the performance of a CNN without increasing its depth nor its width. It consists of expanding the linear kernel function, used at different levels of a CNN. The expansion is performed by combining multiple polynomial kernels with different degrees. By doing so, we allow the network to automatically learn the suitable kernel for the specific target task. The network can either uses one specific kernel or a combination of multiple kernels. In the latter case we will have a kernel in the form of a Taylor series kernel. This kernel function is more sensitive to subtle details than the linear one and is able to better fit the input data. The sensitivity to subtle visual details is a key factor for a better facial expression recognition. Furthermore, this method uses the same number of parameters as a convolution layer or a dense layer. The experiments conducted on FER datasets show that the use of our method allows the network to outperform ordinary CNNs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Burges, C.J., Scholkopf, B., Smola, A.J.: Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge (1999)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. learn. 20(3), 273–297 (1995)
Cui, Y., Zhou, F., Wang, J., Liu, X., Lin, Y., Belongie, S.: Kernel pooling for convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2930 (2017)
Goodfellow, I.J., et al.: Challenges in representation learning: a report on three machine learning contests. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013. LNCS, vol. 8228, pp. 117–124. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-42051-1_16
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 (ICCV) (2015)
Jayasumana, S., Ramalingam, S., Kumar, S.: Kernelized classification in deep networks. arXiv preprint arXiv:2012.09607 (2020)
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)
Mahmoudi, M.A., Chetouani, A., Boufera, F., Tabia, H.: Deep kernelized network for fine-grained recognition. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13110, pp. 100–111. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92238-2_9
Mahmoudi, M.A., Chetouani, A., Boufera, F., Tabia, H.: Kernelized dense layers for facial expression recognition. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2226–2230 (2020)
Mahmoudi, M.A., Chetouani, A., Boufera, F., Tabia, H.: Learnable pooling weights for facial expression recognition. Pattern Recogn. Lett. 138 (2020)
Mahmoudi, M.A., Chetouani, A., Boufera, F., Tabia, H.: Improved bilinear model for facial expression recognition. Pattern Recogn. Artif. Intell. 1322, 47 (2021)
Mahmoudi, M.A., Chetouani, A., Boufera, F., Tabia, H.: Taylor series kernelized layer for fine-grained recognition. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 1914–1918. IEEE (2021)
Mahmoudi, M.A., Chetouani, A., Boufera, F., Tabia, H.: Kernel-based convolution expansion for facial expression recognition. Pattern Recogn. Lett. 160, 128–134 (2022)
Mahmoudi, M.A.: Deep learning for emotion recognition. Ph.D. thesis (2022)
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.R.: Fisher discriminant analysis with kernels. In: Neural networks for signal processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (cat. no. 98th8468), pp. 41–48. IEEE (1999)
Robert, C.: Machine learning, a probabilistic perspective (2014)
Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)
Schölkopf, B., Smola, A.J., Bach, F., et al.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
Wang, C., Yang, J., Xie, L., Yuan, J.: Kervolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 31–40 (2019)
Zhang, Z., Luo, P., Loy, C.C., Tang, X.: From facial expression recognition to interpersonal relation prediction. Int. J. Comput. Vis. 126(5), 550–569 (2017). https://doi.org/10.1007/s11263-017-1055-1
Zoumpourlis, G., Doumanoglou, A., Vretos, N., Daras, P.: Non-linear convolution filters for cnn-based learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4761–4769 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mahmoudi, M.A., Boufera, F., Chetouani, A., Tabia, H. (2023). Expanding Convolutional Neural Network Kernel for Facial Expression Recognition. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-28540-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28539-4
Online ISBN: 978-3-031-28540-0
eBook Packages: Computer ScienceComputer Science (R0)