Efficiency and Accuracy: Comparison of Pir, Opencv With a Webcam, and Raspberry Pi

2025;
: pp. 77 - 82
1
Lviv Polytechnic National University, Ukraine
2
Lviv Polytechnic National University, Ukraine
3
Lviv Polytechnic National University, Ukraine
4
Institute of Solid State Physics, University of Latvia, Latvia

This paper is dedicated to developing and evaluating the facial recognition system, focusing on its effectiveness and operational reliability under real-world conditions. The choice of the Raspberry Pi hardware platform for implementing the system has been justified by its capability to process video streams in real time, as well as its compatibility with the high-quality Raspberry Pi Camera V2, which enables the acquisition of images with sufficient resolution for the proper functioning of computer vision algorithms. The implementation of a prototype of the studied system has been presented, encompassing the development of a face detection algorithm, hardware configuration, integration of the OpenCV, Dlib, and Picamera2 libraries, as well as the use of pre-trained models for accurate detection of facial key points. The developed algorithm has been adapted and optimized to consider the limited hardware resources of the Raspberry Pi platform, ensuring high accuracy and real-time image processing performance.

  1. Klym, H., Dunets, R., Horbatyi, I., & Diachok, R. (2018). Security subsystem and smart home management system. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT), 194-197. IEEE. DOI: https://doi.org/10.1109/DESSERT.2018. 8409126.
  2. R. Diachok, R. Dunets and H. Klym, (2018). System of detection and scanning bar codes from Raspberry Pi web camera, IEEE 9th International Conference on Depen dable Systems, Services and Technologies (DESSERT), 2018,              184-187, DOI:       https://doi.org/ 10.1109/DESSERT.2018.8409124
  3. Amuta, E. O., Sobola, G. O., Eseabasi, O., Dike, H. N., Matthew, S., Agbetuyi, A. F., & Wara, S. T. (2024). Motion Detection System Using Passive Infrared Technology. In IOP Conference Series: Earth and Environmental Science. IOP Publishing. DOI: https://doi.org/10.1088/1755-1315/1342/1/012001
  4. Luna, J. I. V., Rangel, F. J. S., Francisco, J., Aceves, C., Guzmán, G. S., & Vargas, V. N. T. (2020) Motion detection using a Raspberry Pi 4 and OpenCV. DOI: https://doi.org/10.1088/1755-1315/1342/1/012001
  5. Ananda, W. R., Lubis, A. J., & Khair, U. (2025). Implementation of Motion Sensors and Buzzers on Robots to Detect Object Movement. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 4(2), 1354-1361. DOI:https://doi.org/10.59934/jaiea.v4i2.907.
  6. [Bagye, Wire, Khairul Imtihan, and Maulana Ashari (2024). Comparison Of Various Types Of Pir Motion Sensors For Nodemcu Esp32 Cam Image Capturing Devices. Jurnal Informatika dan Rekayasa Elektronik 7.2: 471-477. DOI: https://doi.org/10.36595/jire.v7i2.1344.
  7. Jamil Alsayaydeh, Jamil Abedalrahim,  (2025). Handwritten text recognition system using Raspberry Pi with OpenCV TensorFlow. International Journal of Electrical & Computer Engineering, 2291-2303. DOI: https://doi.org/10.11591/ijece.v15i2.pp2291-2303.
  8. Park, Y. (2025). Development  of  Raspberry  Pi Autonomous Car using OpenCV and NVIDIA CNN Model. The Journal of the Convergence on Culture Technology, 11(2), 367-378. DOI: https://doi.org/ 10.17703/JCCT.2025.11.2.367
  9. Banerjee, P., Datta, P., Pal, S., Chakraborty, S., Roy, A., Poddar, S., Ghosh, A. (2022). Home Security System Using Raspberry Pi. In Advanced Energy and Control Systems: Select Proceedings of 3rd International Conference,        ESDA        2020,        167-176        DOI:https://doi.org/10.1007/978-981-16-7274-3_14
  10. Nadafa, R. A., Hatturea, S. M., Bonala, V. M., & Naikb, S. P. (2020). Home security against human intrusion using Raspberry Pi. Procedia Computer Science, 167, 1811- 1820. DOI: https://doi.org/10.1016/j.procs.2020.03.200
  11. F. Faisal and S. A. Hossain (2019), Smart Security System Using Face Recognition on Raspberry Pi, 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 1-8,DOI: https://doi.org/10.1109/SKIMA47702.2019.8982466
  12. Silalahi, L. M., Simanjuntak, I. U. V., Silaban, F. A., Budiyanto, S., & Ikhsan, M. (2020, December). Integration of opencv raspberry pi 3b+ and camera sensor in access control of vehicle ignition key system. IOP Conference Series: Materials Science and Engineering, 909(1), 012002. DOI: https://doi.org/10.1088/1757-899X/909/1/012002.