融合注意力机制的轻量级戴口罩人脸识别算法

Lightweight Masked Face Recognition Algorithm Incorporating Attention Mechanism

COVID-19的全球化大流行使得佩戴口罩出行成为人们生活中的常态,这种防疫措施给人脸识别算法带来了新的挑战。针对这一问题,提出了一种口罩遮挡下的轻量级人脸识别算法,该算法改进GhostNet为主干特征提取网络;提出了融合空间注意力机制的FocusNet特征加强提取网络,使模型聚焦于未被口罩遮挡的上半脸区域;针对当前口罩遮挡人脸数据集不充分的问题,提出了一种采用三维人脸网络生成添加口罩遮挡的数据增强方法。实验表明,所提出的改进模型与基准模型相比,模型参数量降低84%的同时,戴口罩人脸的识别率提升4.29个百分点,较好地平衡了速度与精度。

The globalization pandemic of COVID-19 has made wearing masks to become a norm in people’s lives, and this preventive measure brings new challenges to face recognition algorithms. To address this problem, this paper proposes a lightweight masked face recognition algorithm. Firstly, this network introduces GhostNet as the backbone feature extraction network to improve the recognition speed. Secondly a FocusNet feature enhancement extraction network incorporating spatial attention mechanism is proposed to make the model focus on the upper half of the face region that are not covered by masks. Then, in order to overcome the problem of inadequate masked face dataset, a data augmentation method using 3D face mesh is proposed to add face mask. Finally, the experimental results show that, compared with the benchmark model, the proposed model reduces the number of model parameters by 84%, while the AP of masked face recognition increases by 4.29 percentage points, which better balances speed and accuracy.