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. 2022 Feb 12:2022:5137513.
doi: 10.1155/2022/5137513. eCollection 2022.

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

Tahir Hussain  1 , Dostdar Hussain  2 , Israr Hussain  2 , Hussain AlSalman  3 , Saddam Hussain  4 , Syed Sajid Ullah  5 , Suheer Al-Hadhrami  6
Affiliations

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

Tahir Hussain et al. Comput Math Methods Med. .

Retraction in

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.

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Conflict of interest statement

There are no conflicts of interest associated with publishing this paper.

Figures

Figure 1
Figure 1
Block diagram architecture.
Figure 2
Figure 2
USB web camera.
Figure 3
Figure 3
Raspberry Pi 4 model B+ control module.
Figure 4
Figure 4
E-mail notification.
Figure 5
Figure 5
Haar features.
Figure 6
Figure 6
Feature extraction.
Figure 7
Figure 7
Face recognition work flow.
Figure 8
Figure 8
The detected frontal and profile faces.
Figure 9
Figure 9
The illumination variation.
Figure 10
Figure 10
Database face images.
Figure 11
Figure 11
ResNet-50 model with SVM.
Figure 12
Figure 12
VGG-16 model with SVM.
Figure 13
Figure 13
ReLU operation.
Figure 14
Figure 14
System work flow.
Figure 15
Figure 15
Converting grayscale to decimal.
Figure 16
Figure 16
Local binary pattern histogram for face description.
Figure 17
Figure 17
Flow chart of LBPH algorithm.
Figure 18
Figure 18
Testing time graph of pretrained CNN and LBPH model.
Figure 19
Figure 19
Response time for recognition of a person.
Figure 20
Figure 20
Model accuracy.
Figure 21
Figure 21
Model loss.
Figure 22
Figure 22
Model recognizing faces.
See this image and copyright information in PMC

References

    1. Nareshkumar R., Kamat A., Shinde D. Smart door security control system using Raspberry Pi. International Journal of Innovations & Advancement in Computer Science (IJIACS) . 2017;6:499–503.
    1. Najmurrokhman A., Kusnandar K., Krama A. B., Djamal E. C., Rahim R. Development of a secured room access system based on face recognition using Raspberry Pi and Android based smartphone. EDP Sciences . 2018;197:p. 11008. doi: 10.1051/matecconf/201819711008. - DOI
    1. Perdana A. B., Prahara A. Face recognition using light-convolutional neural networks based on modified Vgg16 model. 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM); 2019; Medan, Indonesia. pp. 1–4. - DOI
    1. Dabhi M. K., Pancholi B. K. Face detection system based on Viola-Jones algorithm. International Journal of Science and Research (IJSR) . 2016;5(4):62–64. doi: 10.21275/v5i4.NOV162465. - DOI
    1. Agarwal M., Agrawal H., Jain N., Kumar M. Face recognition using principle component analysis, eigenface and neural network. 2010 International Conference on Signal Acquisition and Processing; 2010; Bangalore, India. pp. 310–314. - DOI

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