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

Tahir Hussain1, Dostdar Hussain2, Israr Hussain2

  • 1Department of Computer Science and Communication Engineering, National Cheng Kung University, 70101, Taiwan.

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Summary

This study introduces an Internet of Things (IoT) and deep learning (DL) system for secure, touchless medical authentication using facial recognition. The system achieves 99.56% accuracy, overcoming issues of identity fraud and disease spread.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • The integration of Internet of Things (IoT) and deep learning (DL) is rapidly advancing, offering significant potential in healthcare applications.
  • Existing security systems in medical environments face challenges such as identity fraud, password/key management, and disease transmission via touch-based authentication.

Purpose of the Study:

  • To propose and evaluate an IoT-based intelligent control system for medical authentication using deep learning models.
  • To enhance security in healthcare settings by implementing a touchless facial recognition system.

Main Methods:

  • The system utilizes a Raspberry Pi (RPi) as the central controller, connected to smart doors and a camera module for image capture.
  • Face detection is performed using Haar cascade techniques, followed by facial feature extraction with pre-trained CNN models (ResNet-50, VGG-16) and the Linear Binary Pattern Histogram (LBPH) algorithm.
  • Face recognition is achieved using a Support Vector Machine (SVM) classifier, with successful authentication unlocking the door.

Main Results:

  • The proposed system demonstrated a high accuracy rate of 99.56% in comparative studies.
  • Upon successful authentication, the door unlocks; otherwise, it remains locked, and notifications with face images are sent.
  • Detected person's name and time information are stored in an SQL database for record-keeping.

Conclusions:

  • The developed IoT and DL-based facial recognition system effectively enhances security in medical and healthcare environments.
  • The touchless authentication approach addresses critical issues related to identity fraud and the spread of infectious diseases.
  • The system's high accuracy and efficient implementation using Raspberry Pi offer a practical solution for smart medical authentication.