Expanding Convolutional Neural Network Kernel for Facial Expression Recognition
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.