Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Tahir Hussain; Dostdar Hussain; Israr Hussain; Hussain AlSalman; Saddam Hussain; Syed Sajid Ullah; Suheer Al-Hadhrami

doi:10.1155/2022/5137513

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Comput Math Methods Med. 2022 Feb 12:2022:5137513. doi: 10.1155/2022/5137513. eCollection 2022.

Authors

Tahir Hussain¹, Dostdar Hussain², Israr Hussain², Hussain AlSalman³, Saddam Hussain⁴, Syed Sajid Ullah⁵, Suheer Al-Hadhrami⁶

Affiliations

¹ Department of Computer Science and Communication Engineering, National Cheng Kung University, 70101, Taiwan.
² Department of Computer Sciences, Karakoram International University, Gilgit 15100, Pakistan.
³ Department of Computer Science, King Saud University, Riyadh 11543, Saudi Arabia.
⁴ Department of Information Technology, Hazara University, Mansehra, Pakistan.
⁵ Department of Information and Communication Technology, University of Agder, Norway.
⁶ Computer Engineering Department, Engineering College, Hadhramout University, Hadhramaut, Yemen.

Abstract

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.

Publication types

Retracted Publication

MeSH terms

Algorithms
Automated Facial Recognition*
COVID-19
Computer Security
Computer Simulation
Databases, Factual
Deep Learning*
Equipment Design
Humans
Internet of Things*
Pattern Recognition, Automated
SARS-CoV-2
Support Vector Machine