An Intelligent Health Control Security Robotic System


  • Syed Danish Ahmad Sharifi POF factory wah cantt pakistan
  • Muhammand Usman
  • Eman Gul


Face Detection, Facemask Detection, Audio Features, Robot


Nowadays, scientific knowledge is constantly bringing comfort and change to everyday life but the entire world is facing a major health crisis as a result of coronavirus disease transmission. According to the World Health Organization (WHO), wearing a mask on the face in public areas is one effective method of protection against COVID. Autonomous robots have become a prominent technology in recent years, with applications in a variety of fields. Robots are utilized to complete tasks more quickly than humans. Generally, robots are smarter with endless energy levels, and are precise in task management. Therefore, propose the methodology of this work is focused on developing a robot-based COVID-19 protection system. The proposed robot-based COVID-19 protection system consists of five core steps: 1) Person Identification, 2) Vaccination Checking 3) Face Recognition 4) Face Mask Detection, and 5) Temperature Checking. Person identification is performed using HOG to detect faces and machine learning classifier SVM for identification of the person. Then vaccination status is checked. To check vaccination status, it gets the name from face recognition and matches with the names in the database and gets a value from the vaccination column to show if the person is vaccinated or not and facemask detection is performed by facemask detector using Keras Mobile-Net architecture. The temperature is checked using the MLX90614 temperature sensor. The robot performs all these functions by speaking and by displaying them on the LCD screen. Artificial Intelligence on basis of deep learning and neural networks could help in fighting Corona Virus in many ways. The purpose of this system is to use a security robot instead of a security guard in organizations such as universities, colleges, schools, offices, software houses, and other organizations. The proposed system performed better compared to the existing systems as it achieves 99% precision and a 0.01% error rate.


L. Yang, C. Fu, Y. Li, and L. Su, ―Survey and study on intelligent monitoring and health management for large civil structure,‖ International Journal of Intelligent Robotics and Applications, vol. 3, no. 3, pp. 239-254, 2019.

K. W. J. I. T. Bowyer, and s. magazine, ―Face recognition technology: security versus privacy,‖ vol. 23, no. 1, pp. 9-19, 2004.

A. Muiz Fayyaz, M. Kolivand, J. Alyami, S. Roy, and A. Rehman, "Computer Vision-Based Prognostic Modelling of COVID-19 from Medical Imaging," Prognostic Models in Healthcare: AI and Statistical Approaches, pp. 25-45: Springer, 2022.

W. H. Organization, ―COVID-19 transmission and protective measures,‖ World Health Organization. At https://www.who. int/westernpacific/emergencies/COVID-19/information/transmission-protective-measures, 2020.

R. Vaishya, M. Javaid, I. H. Khan, A. J. D. Haleem, M. S. C. Research, and Reviews, ―Artificial Intelligence (AI) applications for COVID-19 pandemic,‖ vol. 14, no. 4, pp. 337-339, 2020.

J. Gee, P. Marquez, J. Su, G. M. Calvert, R. Liu, T. Myers, N. Nair, S. Martin, T. Clark, and L. Markowitz, ―First month of COVID-19 vaccine safety monitoring—United States, December 14, 2020–January 13, 2021,‖ Morbidity and mortality weekly report, vol. 70, no. 8, pp. 283, 2021.

S. V. Militante, and N. V. Dionisio, "Real-time facemask recognition with alarm system using deep learning." pp. 106-110.

A. M. Fayyaz, K. A. Al-Dhlan, S. U. Rehman, M. Raza, W. Mehmood, M. Shafiq, and J.-G. Choi, ―Leaf Blights Detection and Classification in Large Scale Applications,‖ INTELLIGENT AUTOMATION AND SOFT COMPUTING, vol. 31, no. 1, pp. 507-522, 2022.

P. J. J. o. i. Brey, communication, and e. i. society, ―Ethical aspects of facial recognition systems in public places,‖ 2004.

M. A. Shereen, S. Khan, A. Kazmi, N. Bashir, and R. J. J. o. a. r. Siddique, ―COVID-19 infection: Emergence, transmission, and characteristics of human coronaviruses,‖ vol. 24, pp. 91-98, 2020.

P. Li, Y. Xu, Y. Wei, Y. J. I. T. o. P. A. Yang, and M. Intelligence, ―Self-correction for human parsing,‖ 2020.

Y. Sun, B. Xue, M. Zhang, G. G. Yen, and J. J. I. t. o. c. Lv, ―Automatically designing CNN architectures using the genetic algorithm for image classification,‖ vol. 50, no. 9, pp. 3840-3854, 2020.

D. K. Yadav, "Efficient method for moving object detection in cluttered background using Gaussian Mixture Model." pp. 943-948.

W. Wójcik, K. Gromaszek, M. J. F. R.-S. C. Junisbekov, Subspace Projection, and E. Methods, ―Face recognition: Issues, methods and alternative applications,‖ pp. 7-28, 2016.

P. Viola, and M. J. J. I. j. o. c. v. Jones, ―Robust real-time face detection,‖ vol. 57, no. 2, pp. 137-154, 2004.

J. Mahajan, and A. Paithane, "Face detection on distorted images by using quality HOG features." pp. 439-444.

Y.-L. Tian, T. Kanade, and J. F. Cohn, "Facial expression analysis," Handbook of face recognition, pp. 247-275: Springer, 2005.

A. Liang, W. Liu, L. Li, M. R. Farid, and V. Le, "Accurate facial landmarks detection for frontal faces with extended tree-structured models." pp. 538-543.

E. W. J. h. m. w. c. Weisstein, ―Affine transformation,‖ 2004.

G. L. Wells, B. J. M. Hryciw, and Cognition, ―Memory for faces: Encoding and retrieval operations,‖ vol. 12, no. 4, pp. 338-344, 1984.

A. Dosovitskiy, J. Tobias Springenberg, and T. Brox, "Learning to generate chairs with convolutional neural networks." pp. 1538-1546.

H. A. Rowley, S. Baluja, T. J. I. T. o. p. a. Kanade, and m. intelligence, ―Neural network-based face detection,‖ vol. 20, no. 1, pp. 23-38, 1998.

"Jessica Li (, Accessed by 7/9/2022."

J. Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, ―A distinctive approach in brain tumor detection and classification using MRI,‖ Pattern Recognition Letters, vol. 139, pp. 118-127, 2020.

J. Amin, M. Sharif, M. Yasmin, H. Ali, and S. L. Fernandes, ―A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions,‖ Journal of Computational Science, vol. 19, pp. 153-164, 2017.

M. I. Sharif, J. P. Li, J. Amin, and A. Sharif, ―An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network,‖ Complex & Intelligent Systems, pp. 1-14, 2021.

T. Saba, A. S. Mohamed, M. El-Affendi, J. Amin, and M. Sharif, ―Brain tumor detection using fusion of hand crafted and deep learning features,‖ Cognitive Systems Research, vol. 59, pp. 221-230, 2020.

J. Amin, M. Sharif, M. Raza, T. Saba, and M. A. Anjum, ―Brain tumor detection using statistical and machine learning method,‖ Computer methods and programs in biomedicine, vol. 177, pp. 69-79, 2019.

J. Amin, M. Sharif, M. Raza, and M. Yasmin, ―Detection of brain tumor based on features fusion and machine learning,‖ Journal of Ambient Intelligence and Humanized Computing, pp. 1-17, 2018.

J. Amin, M. Sharif, and M. Yasmin, ―A review on recent developments for detection of diabetic retinopathy,‖ Scientifica, vol. 2016, 2016.




How to Cite

Syed Danish Ahmad Sharifi, Muhammand Usman, & Eman Gul. (2023). An Intelligent Health Control Security Robotic System. University of Wah Journal of Computer Science, 4(1), 17–30. Retrieved from