Research on multi-scale face detection based on graph embedded Swin-Transformer

The existing Swin-Transformer has achieved great success in the field of object detection due to its lightweight, hierarchical and efficient long-distance dependency modelling advantages. However, in complex scenes, the impact of scale changes seriously restricts the performance of face detection. Therefore, this article proposes a multi-scale face detection network based on graph embedding Swin-Transformer. This network fully utilises multi-scale features and contextual information, effectively improving the performance of multi-scale face detection. Specifically, we designed a multi-feature fusion module based on Swin Transformer, which effectively integrates deep features of advanced information with shallow texture features. In addition, to effectively model the contextual information of the target, we use embedding graph convolution operations. We conducted extensive comparative experiments on publicly available data sets, and the experimental results showed that the proposed model achieved higher recognition performance.