论文标题
HIMFR：通过脸部涂上的杂种蒙面的脸部识别
HiMFR: A Hybrid Masked Face Recognition Through Face Inpainting
论文作者
论文摘要
要识别蒙面的脸，可能的解决方案之一可能是首先恢复面部的遮挡部分，然后应用面部识别方法。受到最新图像介绍方法的启发，我们提出了端到端混合遮罩的面部识别系统，即HIMFR，由三个重要部分组成：蒙面的面部探测器，脸部涂上涂料和脸部识别。蒙面的面部探测器模块应用了验证的视觉变压器（VIT \ _B32），以检测面部是否被掩盖的面孔覆盖。该模块使用基于生成对抗网络（GAN）的微调图像介绍模型来恢复面部。最后，基于VIT的混合面部识别模块具有有效的NETB3骨架识别面部。我们已经在四个不同的公开数据集上实施并评估了我们提出的方法：Celeba，ssdmnv2，mafa，{pubfig83}与本地收集的小数据集，即面对5。全面的实验结果表明，提出的HIMFR方法具有竞争性能的功效。代码可从https://github.com/mdhosen/himfr获得
To recognize the masked face, one of the possible solutions could be to restore the occluded part of the face first and then apply the face recognition method. Inspired by the recent image inpainting methods, we propose an end-to-end hybrid masked face recognition system, namely HiMFR, consisting of three significant parts: masked face detector, face inpainting, and face recognition. The masked face detector module applies a pretrained Vision Transformer (ViT\_b32) to detect whether faces are covered with masked or not. The inpainting module uses a fine-tune image inpainting model based on a Generative Adversarial Network (GAN) to restore faces. Finally, the hybrid face recognition module based on ViT with an EfficientNetB3 backbone recognizes the faces. We have implemented and evaluated our proposed method on four different publicly available datasets: CelebA, SSDMNV2, MAFA, {Pubfig83} with our locally collected small dataset, namely Face5. Comprehensive experimental results show the efficacy of the proposed HiMFR method with competitive performance. Code is available at https://github.com/mdhosen/HiMFR
