Fully connected layer is an essential component of Convolutional Neural Networks (CNNs), which demonstrates its efficiency in computer vision tasks. The CNN process usually starts with convolution and pooling layers that first break down the input images into features, and then analyze them independently. The result of this process feeds into a fully connected neural network structure which drives the final classification decision. In this paper, we propose a Kernelized Dense Layer (KDL) which captures higher order feature interactions instead of conventional linear relations. We apply this method to Facial Expression Recognition (FER) and evaluate its performance on RAF, FER2013 and ExpW datasets. The experimental results demonstrate the benefits of such layer and show that our model achieves competitive results with respect to the state-of-the-art approaches.

中文翻译：

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用于面部表情识别的内核化密集层

全连接层是卷积神经网络 (CNN) 的重要组成部分，它展示了其在计算机视觉任务中的效率。CNN 过程通常从卷积和池化层开始，首先将输入图像分解为特征，然后独立分析它们。这个过程的结果输入一个完全连接的神经网络结构，驱动最终的分类决策。在本文中，我们提出了一个核化密集层 (KDL)，它捕获高阶特征交互而不是传统的线性关系。我们将此方法应用于面部表情识别 (FER) 并评估其在 RAF、FER2013 和 ExpW 数据集上的性能。