Abstract
In response to the global COVID-19 pandemic, there has been a critical demand for protective measures, with face masks emerging as a primary safeguard. The approach involves a two-fold strategy: first, recognizing the presence of a face by detecting faces, and second, identifying masks on those faces. This project utilizes deep learning to create a model that can detect face masks in real-time streaming video as well as images. Face detection, a facet of object detection, finds applications in diverse fields such as security, biometrics, and law enforcement. Various detector systems worldwide have been developed and implemented, with convolutional neural networks chosen for their superior performance accuracy and speed in object detection. Experimental results attest to the model's excellent accuracy on test data. The primary focus of this research is to enhance security, particularly in sensitive areas. The research paper proposes a rapid image pre-processing method with masks centred on faces. Employing feature extraction and Convolutional Neural Network, the system classifies and detects individuals wearing masks. The research unfolds in three stages: image pre-processing, image cropping, and image classification, collectively contributing to the identification of masked faces. Continuous surveillance through webcams or CCTV cameras ensures constant monitoring, triggering a security alert if a person is detected without a mask.
Abstract (translated)
为了应对全球新冠疫情，人们普遍要求采取保护措施，口罩已成为最主要的保护方式。该方法采用双重策略：首先，通过检测人脸来识别存在的人脸，然后在对人脸进行识别时，确定口罩。本项目利用深度学习创建了一个可以实时检测戴口罩情况的模型。人脸检测，作为物体检测的一个方面，在安全、生物识别和执法等领域有广泛应用。世界各地已经开发并实施了许多检测系统，而卷积神经网络因其卓越的检测性能和速度而备受选择。实验结果证实了模型在测试数据上的卓越准确性。 本项目的研究重点是提高安全性，特别是敏感区域的安全性。研究论文提出了一种快速预处理图像的方法，其中口罩居中。利用特征提取和卷积神经网络，系统对佩戴口罩的人进行分类和检测。研究过程包括图像预处理、图像裁剪和图像分类，共同致力于识别戴口罩的脸孔。通过摄像头或闭路电视的持续监控，确保持续监控，如果一个人在没有戴口罩的情况下被检测到，则会触发安全警报。
URL
https://arxiv.org/abs/2311.10408
