FGENet: a lightweight facial expression recognition algorithm based on FasterNet

To solve the problem of poor recognition accuracy due to the complex network structure in the facial expression recognition algorithm, an improved lightweight network based on FasterNet is proposed: FGENet. Firstly, the PConv in the FasterNet block module is replaced with the RFE module to enhance the network’s ability to perceive key features of facial expressions. Secondly, the GSConv module is introduced into the residual branch, which helps to strengthen the feature modeling of different regions and improves the expression feature extraction ability of the network. Finally, the ECA attention mechanism is introduced into the Ghost module to design the deep attention mechanism DEA, which is applied to the standard convolution after global pooling to better capture the global context information. FGENet is designed to keep the network lightweight while improving performance. Through a series of experiments, it has been proven that FGENet has achieved performance improvements on the three data sets of Fer2013, CK+ and RAF, reaching recognition accuracy rates of 70.49%, 97.89% and 86.72% respectively, further verifying its effectiveness in facial expression recognition tasks.