Department of Computer Science and Communication Engineering, National Cheng Kung University, 70101, Taiwan.
Department of Computer Sciences, Karakoram International University, Gilgit 15100, Pakistan.
Comput Math Methods Med. 2022 Feb 12;2022:5137513. doi: 10.1155/2022/5137513. eCollection 2022.
Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.
物联网（IoT）与深度学习（DL）正在迅速发展，并在许多应用中发挥着重要作用，包括医疗和保健系统。它可以帮助用户在增强无接触认证方面获得优势，特别是在传播 2019 年冠状病毒病（COVID-19）等传染病方面。尽管有许多可用的安全系统，但它们都存在一个或多个问题，例如身份欺诈、密钥和密码丢失，或通过触摸认证工具传播疾病。为了克服这些问题，提出了基于物联网的基于深度学习模型的智能控制医疗认证系统，以有效提高医疗和保健场所的安全系数。这项工作应用物联网与深度学习模型来识别人脸，以在智能控制医疗系统中进行认证。我们使用树莓派（RPi），因为它成本低，在这个系统中充当主控制器。使用 RPi 的通用输入/输出（GPIO）引脚安装智能控制系统也增强了智能锁的防盗功能，RPi 与智能门相连。对于用户认证，使用摄像头模块捕捉人脸图像，并与数据库图像进行比较以获取访问权限。所提出的方法使用 Haar 级联技术进行人脸检测，而对于人脸识别，系统包括以下步骤。第一步是人脸特征提取步骤，使用预训练的卷积神经网络模型（ResNet-50 和 VGG-16）以及线性二进制模式直方图（LBPH）算法完成。第二步是分类步骤，可以使用支持向量机（SVM）分类器完成。只有分类为真实的人脸才能解锁门；否则，门被锁定，系统会将检测到的人脸图像发送到家庭/医疗场所的通知电子邮件，并将检测到的人员姓名和时间信息存储在 SQL 数据库中。与一些不同的相关方法相比，这项工作的比较研究表明，该方法的准确率达到了 99.56%。
