A Visual Self-attention Mechanism Facial Expression Recognition Network beyond Convnext
This work addresses facial expression recognition for AI applications, but it is incremental as it builds on existing ConvNeXt methods with added components.
The paper tackles challenges in facial expression recognition, such as uneven dataset distribution and inter-class similarity, by proposing a Conv-cut network that combines truncated ConvNeXt with a Detail Extraction Block and Self-Attention, achieving state-of-the-art performance on RAF-DB and FERPlus datasets.
Facial expression recognition is an important research direction in the field of artificial intelligence. Although new breakthroughs have been made in recent years, the uneven distribution of datasets and the similarity between different categories of facial expressions, as well as the differences within the same category among different subjects, remain challenges. This paper proposes a visual facial expression signal feature processing network based on truncated ConvNeXt approach(Conv-cut), to improve the accuracy of FER under challenging conditions. The network uses a truncated ConvNeXt-Base as the feature extractor, and then we designed a Detail Extraction Block to extract detailed features, and introduced a Self-Attention mechanism to enable the network to learn the extracted features more effectively. To evaluate the proposed Conv-cut approach, we conducted experiments on the RAF-DB and FERPlus datasets, and the results show that our model has achieved state-of-the-art performance. Our code could be accessed at Github.