Lightweight attention convolutional neural network through network slimming for robust facial expression recognition

Abstract

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.