Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

Abstract

The relationships among different subregions in facial images and their varying contributions to facial expression recognition indicate that using a fixed subregion weighting scheme would result in a substantial loss of valuable information. To address this issue, we propose a facial expression recognition network called BGA-Net, which combines bidirectional gated recurrent units (BiGRUs) with an attention mechanism. Firstly, a convolutional neural network (CNN) is employed to extract feature maps from facial images. Then, a sliding window cropping strategy is applied to divide the feature maps into multiple subregions. The BiGRUs are utilized to capture the dependencies among these subregions. Finally, an attention mechanism is employed to adaptively focus on the most discriminative regions. When evaluated on CK+, FER2013, and JAFFE datasets, our proposed method achieves promising results.