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To address the limited feature extraction capability of convolutional neural networks and the issue of inaccurate localization of facial features, which leads to low recognition rates, this paper proposes an enhanced pyramid convolutional attention network, namely PCAF-Net. The network adopts Pyconvresnet50 as its backbone, incorporates the IMPy module to enhance feature extraction capability, optimizes the PyConv block to improve multi-scale feature extraction, integrates Coordinate Attention to precisely locate facial key areas, and utilizes the ArcFace loss function to enhance expression discrimination. To evaluate its performance, experiments were conducted using the Fer2013 and CK+ datasets. The findings reveal that Coordinate Attention and ArcFace loss functions exhibit superior capabilities in terms of recognition and classification compared to alternative attention and loss functions. It is noteworthy that PCAFNet achieves recognition rates of 71.58% and 92.02% on the Fer2013 and CK+ datasets, respectively, surpassing stateof-the-art networks without an increase in the number of parameters.
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