基于迁移学习技术的部分人脸识别

S. Watcharabutsarakham, S. Marukatat, Supphachoke Suntiwichaya, Chanchai Junlouchai
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引用次数: 0

摘要

在当今世界,人们出门都戴着口罩,所以人脸检测和人脸识别模型需要考虑到这一点。人脸识别已经得到了广泛的研究,使用了各种算法。自2019年冠状病毒病(COVID-19)疫情在泰国蔓延以来,我们使用人脸识别模型提醒人们戴口罩。这是因为当人们外出时,他们很可能会接触到用于身份验证和授权的面部图像检测和分类方法。在本文中,我们使用迁移学习,如YOLOv3,并使用公共数据集和捐赠数据集进行训练。我们的模型识别人脸的准确率为98.7%,识别包括戴口罩在内的人脸的准确率为92.7%。
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Partial Facial Identification using Transfer Learning Technique
In today’s world, people go outside wearing a face mask, so face detection and face recognition models need to take this into account. Facial recognition has been researched widely with various algorithms. Since the coronavirus disease of 2019 (COVID-19) outbreak has spread across Thailand, our use of face recognition models has reminded people to wear a face mask. This is because when people go outside, they are likely to be exposed to facial image detection and classification methods which are used for authentication and authorization. In this paper, we use transfer learning such as YOLOv3 and training with public datasets and donation datasets. Our models can recognize faces with a 98.7% accuracy rate and identify faces including those with face masks-with a 92.7% accuracy rate.
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