International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Real Time Face Detection using Haar Cascade Classifier Algorithm

Mr. Pavan Kumar UPG Student of MCA, Dr. Ambedkar Institute of Technology, Bangalore, IndiaMrs. Shivaleela SAssistant Professor, Department of MCA, Dr. Ambedkar Institute of Technology, Bangalore, India

Vol 6 No 6 (2022): Volume 6, Issue 6, June 2022 | Pages: 194-197

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 06-07-2022

doi Logo doi.org/10.47001/IRJIET/2022.606026

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Abstract

In biometrics research, face detection and recognition from an image or video is a hot topic. Due to the enormous market potential and application value of face recognition technology, such as in real-time video surveillance systems, it has garnered a lot of attention. Face recognition has a significant impact on surveillance systems, it is widely acknowledged, because it doesn't require object cooperation. Using Python and OpenCV programming, we create a real-time face recognition system based on IP cameras and an image set algorithm. The system is composed of three modules: detection, training, and recognition.

Keywords

OpenCV, Machine Learning


Citation of this Article

Mr. Pavan Kumar U, Mrs. Shivaleela S, “Real Time Face Detection using Haar Cascade Classifier Algorithm” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 6, Issue 6, pp 194-197, June 2022. Article DOI https://doi.org/10.47001/IRJIET/2022.606026

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. C. Thurau and V. Hlavac, “Pose primitive based human action recognition in videos or still images,”
  2. D. Weinland and E. Boyer, “Action recognition using exemplar-based embedding,”
  3. N. I. Cinbis, R. G. Cinbis, and S. Sclaroff, “Learning actions from the Web,”
  4. Acion, Laura, et al., “Using machine learning algorithm to predict use drug treatment.” PloS one12.4 (2017).
  5. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,”
  6. Davis Jesse. The relationship between precision-recall and ROC curves. Conference: Proceedings of the 23rd International Conference on Machine Learning; June; 2006.
  7. N. I. Cinbis, R. G. Cinbis, and S. Sclaroff, “Learning actions from the Web,” in Proc. ICCV, 2009, pp. 995–1002.
  8. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as space-time shapes,” in Proc. ICCV, 2005, pp. 1395–1402.
  9. D. Weinland and E. Boyer, “Action recognition using exemplar-based embedding,” in Proc. CVPR, 2008, pp. 1–7.
  10. Real time face detection by Marcelo, Face detection Stanford University.

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