论文标题
内核密集层用于面部表达识别
Kernelized dense layers for facial expression recognition
论文作者
论文摘要
完全连接的层是卷积神经网络（CNN）的重要组成部分，它证明了其在计算机视觉任务中的效率。 CNN过程通常从首先将输入图像分解为特征的卷积和汇总层开始，然后独立分析它们。该过程的结果进展为完全连接的神经网络结构，该结构推动了最终的分类决策。在本文中，我们提出了一个内核密度层（KDL），该层捕获高阶特征相互作用而不是常规线性关系。我们将此方法应用于面部表达识别（FER），并评估其在RAF，FER2013和EXPW数据集上的性能。实验结果证明了这种层的好处，并表明我们的模型在最先进的方法方面取得了竞争成果。
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.
