AFF-Net: a masked face recognition network based on attention and feature fusion

Abstract

The context of the COVID-19 pandemic has made an increasing number of people accustomed to wearing masks to avoid getting infected. This phenomenon leads to an urgent demand for face recognition with masks, which poses serious challenge for the accuracy of face recognition. This paper seeks to produce a model called AFF-Net based on an attention mechanism and feature fusion and self-attention to address the masked face recognition by designing four compact modules to achieve the optimization of the network structure to solve the problem of unstable recognition rate and the low model generalization ability. Specifically, we fuse the features of different layers in the improved full connected module to assign weights to the channels, and formulate a specific training strategy to improve the feature learning performance. Experiments on representative datasets indicate that the proposed method can outperform the competing method significantly while maintaining high real-time efficiency of masked face recognition.