Facial expression recognition (FER) is a popular research field in cognitive interaction systems and artificial intelligence. Many deep learning methods achieve outstanding performances at the expense of enormous computation workload. Limiting their application in small devices or offline scenarios. To cope with this drawback, this paper proposes the Frequency Multiplication Network (FMN), a deep learning method operating in the frequency domain that significantly reduces network capacity and computation workload. By taking advantage of the frequency domain conversion, this novel deep learning method utilizes multiplication layers for effective feature extraction. In conjunction with the Uniform Rectangular Features (URF), our method further improves the performance and reduces the training effort. On three publicly available datasets (CK+, Oulu, and MMI), our method achieves substantial improvements in comparison to popular approaches.

面部表情识别（FER）是认知交互系统和人工智能领域的热门研究领域。许多深度学习方法以巨大的计算工作量为代价获得了出色的性能。限制其在小型设备或脱机方案中的应用。为了解决这个缺点，本文提出了一种频率倍增网络（FMN），这是一种在频域中运行的深度学习方法，可显着减少网络容量和计算工作量。通过利用频域转换，这种新颖的深度学习方法利用乘法层进行有效的特征提取。结合统一矩形特征（URF），我们的方法进一步提高了性能并减少了训练工作量。在三个公开可用的数据集（CK +，Oulu，