Deep convolutional neural networks (DCNNs) have achieved outstanding results in facial expression recognition (FER). However, their runtime memory and computational resource requirements make it challenging to deploy them on resource-constrained devices, such as mobile devices. In this paper, we propose a novel lightweight attention DCNN (LA-Net) for robust FER, which uses squeeze-and-excitation (SE) modules and the network slimming strategy. First, we combine the SE modules with the CNN network, which assigns a certain weight to each feature channel. This enables LA-Net to focus on learning the prominent facial features, reduce redundant information, and finally extract discriminative features from facial images. Then, we use the network slimming method to further reduce the model’s size, which results in a thin and compact network that uses less runtime memory and computational operations with minimal accuracy loss. The proposed LA-Net model can achieve 95.52%, 87.00% and 100% test accuracy on KDEF, RAF-DB and FERG-DB FER datasets, respectively. The experimental results show that the proposed method achieves better or comparable results than state-of-the-art FER methods and significantly reduces the computational cost and the number of parameters, with better generalization capability and robustness.

深度卷积神经网络（DCNN）在面部表情识别（FER）中取得了出色的成绩。但是，它们的运行时内存和计算资源要求使得将它们部署在资源受限的设备（例如移动设备）上具有挑战性。在本文中，我们提出了一种用于鲁棒FER的新型轻量级注意力DCNN（LA-Net），它使用了挤压和激励（SE）模块和网络瘦身策略。首先，我们将SE模块与CNN网络相结合，然后为每个功能通道分配一定的权重。这使LA-Net能够专注于学习突出的面部特征，减少冗余信息，并最终从面部图像中提取出具有区别性的特征。然后，我们使用网络瘦身方法进一步缩小模型的尺寸，这样就形成了一个精简而紧凑的网络，该网络使用较少的运行时内存和计算操作，并且精度损失最小。所提出的LA-Net模型可以分别在KDEF，RAF-DB和FERG-DB FER数据集上达到95.52％，87.00％和100％的测试精度。实验结果表明，与最新的FER方法相比，该方法可获得更好的结果或可比的结果，并显着降低了计算成本和参数数量，具有更好的泛化能力和鲁棒性。