融合PVT多级特征的口罩人脸识别研究

Research on masked face recognition by fusing multi-level features of PVT

呼吸系统疾病的流行使口罩扮演着重要角色,这给人脸识别算法带来了新的挑战。受到多尺度特征融合模型的启发,提出一种基于金字塔视觉Transformer(Pyramid Vision Transformer,PVT)的提取口罩人脸特征的模型。该模型引入自注意力机制来提取丰富的人脸信息,通过融合PVT多个层级的特征向量,来实现对口罩人脸的多尺度关注,相较于传统特征融合模型,具有更高的识别精度和更少的参数量。此外,模型采用Sub-center ArcFace损失函数来提升鲁棒性。模型在大规模模拟口罩人脸数据集上进行训练,并分别在普通人脸、模拟口罩人脸和真实口罩人脸数据集上进行了测试和评估。实验结果表明,所提出的方法与其他主流方法相比,具有较高的识别精度,是一种有效的口罩人脸识别方法。

The prevalence of respiratory diseases has made masks play an important role,which has brought new challenges to face recognition algorithms.Inspired by the multi-scale feature fusion model,a Pyramid Vision Transformer(PVT)based face mask feature extraction model is proposed.The model introduces self-attention mechanism to extract rich face information,and realizes multi-scale attention to mask faces by fusing multi-level feature vectors of PVT.Compared with traditional feature fusion model,the model has higher recognition accuracy and fewer parameters.In addition,the model adopts Sub-center ArcFace loss function to improve robustness.The model was trained on a large scale simulated mask face dataset,and tested and evaluated on ordinary face,simulated mask face and real mask face dataset respectively.The experimental results show that the proposed method has higher recognition accuracy than other mainstream methods,and is an effective mask face recognition method.