论文标题
使用变压器和卷积网络进行掩盖的面部识别的合奏学习
Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition
论文作者
论文摘要
戴着口罩是我们必须遵循的调整之一，以减少冠状病毒的扩散。不断掩盖面具的面孔促使人们需要理解和研究这种行为如何影响面部识别系统的识别能力。当前面部识别系统在处理不受限制的一般面部识别案例时具有极高的精度，但对被遮挡的面孔的概括不能很好地概括。在这项工作中，我们提出了一个用于面部识别的系统。提出的系统包括两个卷积神经网络（CNN）模型和两个变压器模型。 CNN模型已在面部预训练模型上进行了微调。我们使用多数投票技术来整合四个模型的预测，以识别戴面具的人。已在此工作中创建的合成LFW数据集上对所提出的系统进行了评估。最佳准确性是使用92％精度的结合模型获得的。该识别率的表现优于其他模型的准确性，它显示了识别掩盖面孔的模型的正确性和鲁棒性。代码和数据可从https://github.com/hamzah-luqman/mfr获得
Wearing a face mask is one of the adjustments we had to follow to reduce the spread of the coronavirus. Having our faces covered by masks constantly has driven the need to understand and investigate how this behavior affects the recognition capability of face recognition systems. Current face recognition systems have extremely high accuracy when dealing with unconstrained general face recognition cases but do not generalize well with occluded masked faces. In this work, we propose a system for masked face recognition. The proposed system comprises two Convolutional Neural Network (CNN) models and two Transformer models. The CNN models have been fine-tuned on FaceNet pre-trained model. We ensemble the predictions of the four models using the majority voting technique to identify the person with the mask. The proposed system has been evaluated on a synthetically masked LFW dataset created in this work. The best accuracy is obtained using the ensembled models with an accuracy of 92%. This recognition rate outperformed the accuracy of other models and it shows the correctness and robustness of the proposed model for recognizing masked faces. The code and data are available at https://github.com/Hamzah-Luqman/MFR
