Facial expression recognition has become a newly-emerging topic in recent decades, which has important value in the field of human-computer interaction. In this paper, we present a deep learning based approach, named frequency neural network (FreNet), for facial expression recognition. Different from convolutional neural network in spatial domain, FreNet inherits the advantages of processing image in frequency domain, such as efficient computation and spatial redundancy elimination. First, we propose the learnable multiplication kernel and construct multiple multiplication layers to learn features in frequency domain. Second, a summarization layer is proposed following multiplication layers to further yield high-level features. Third, based on the property of discrete cosine transform (DCT), we utilize multiplication layers and summarization layer to construct the Basic-FreNet, which can yield high-level features on the widely used DCT feature. Finally, to further achieve better performance on Basic-FreNet, we propose the Block-FreNet in which the weight-shared multiplication kernel is designed for feature learning and the block sub-sampling is designed for dimension reduction. The experimental results show that the Block-FreNet not only achieves superior performance, but also greatly reduces the computational cost. To our best knowledge, the proposed approach is the first attempt to fill in the blank of frequency based deep learning model for facial expression recognition.

中文翻译：

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使用频率神经网络进行面部表情识别


面部表情识别已成为近几十年来的新兴课题，在人机交互领域具有重要价值。在本文中，我们提出了一种基于深度学习的方法，称为频率神经网络（FreNet），用于面部表情识别。与空间域的卷积神经网络不同，FreNet继承了频域处理图像的优点，如计算高效、空间冗余消除等。首先，我们提出可学习的乘法内核并构造多个乘法层来学习频域特征。其次，在乘法层之后提出了一个汇总层，以进一步产生高级特征。第三，基于离散余弦变换（DCT）的特性，我们利用乘法层和汇总层构建Basic-FreNet，它可以在广泛使用的DCT特征上产生高级特征。最后，为了进一步在Basic-FreNet上获得更好的性能，我们提出了Block-FreNet，其中权重共享乘法内核被设计用于特征学习，块子采样被设计用于降维。实验结果表明，Block-FreNet不仅取得了优越的性能，而且大大降低了计算成本。据我们所知，所提出的方法是填补基于频率的深度学习模型用于面部表情识别的空白的首次尝试。