Authors : K. Hasini; V. Varshini; A. Shakeena; M. Lalitha; G. D. Harshitha; K. Geethika; B. Bhasker Murali Krishna

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/h4m5ukrb

Scribd : https://tinyurl.com/2kjhp86a

DOI : https://doi.org/10.38124/ijisrt/25apr723

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Facial recognition technology has gained significant traction in security, authentication, and accessibility applications. This study presents the development of a facial recognition system using Local Binary Patterns Histogram (LBPH) for efficient face detection and recognition. The system integrates OpenCV, NumPy, and PIL for image processing and training, leveraging Haar Cascade classifiers for accurate face detection. The model is trained on labeled datasets and utilizes real-time video streaming for face capture and recognition. This approach ensures fast and efficient identification of individuals while maintaining computational efficiency. The project demonstrates a robust and lightweight solution suitable for real-world applications such as attendance systems, access control, and surveillance. The implementation highlights the effectiveness of LBPH in handling variations in lighting, pose, and facial expressions, ensuring accurate recognition. The system is designed to function independently, making it ideal for standalone environments without requiring cloud-based processing. The method ensures low computational overhead, making it accessible for devices with limited hardware capabilities. Additionally, it offers privacy and security advantages by storing and processing data locally. The real-time face recognition system enhances usability and efficiency, providing seamless identification without manual intervention. The results indicate that the system provides high reliability and accuracy even under varying environmental conditions. The LBPH algorithm proves to be a versatile and effective choice for real-world deployment. This research underscores the potential of facial recognition in enhancing security and automation while ensuring ease of use.

Keywords : LBPH Algorithm, Opencv, Numpy, PIL, Haar Cascade Classifier.

References :

1. M. Karunakar, et al., "Smart Attendance Monitoring System (SAMS): A Face Recognition Based Attendance System for Classroom Environment," Int. J. Recent Develop. Sci. Technol., vol. 4, no. 5, pp. 194-201, 2020.
2. A. E. Widjaja, N. J. Harjono, H. Hery, A. R. Mitra, and C. A. Haryani, "Automated Class Attendance Management System using Face Recognition: An Application of Viola-Jones Method," J. Appl. Data Sci., vol. 4, no. 4, 2024.
3. U. R. Ughade, S. M. Gikwad, A. N. Yeole, and A. O. Mulani, "Automatic Attendance System Using Face Recognition," J. Image Process. Intell. Remote Sens., vol. 3, no. 4, 2023.
4. S. K. Jha, A. Tyagi, K. Kumar, and M. Sharma, "Attendance Management System using Face Recognition," Int. J. Eng. Res. Technol. (IJERT), vol. 8, no. 10, 2020.
5. V. S. Sangtani, R. Tak, R. Nagarwal, and R. Yadav, "Smart Attendance System Using Face Recognition," Int. J. Recent Adv. Multidiscip. Topics, vol. 4, no. 6, 2023.
6. V. Gopinath, R. R. Nair, Meharban, L. M. B. Lekshmipriya, and D. S. B., "Automated Attendance System Based on Face Recognition & Live Video Processing," Int. J. Eng. Res. Technol. (IJERT), vol. 9, no. 7, 2021.
7. G. Al-Muhaidhri and J. Hussain, "Smart Attendance System using Face Recognition," Int. J. Eng. Res. Technol. (IJERT), vol. 8, no. 12, 2019.
8. N. K. Dubey, P. M. R., K. Vishal, D. G. H. L., and K. B. R., "Face Recognition Based Attendance System," Int. J. Eng. Res. Technol. (IJERT), vol. 9, no. 6, 2020.
9. Patil, Ajinkya, et al. "Scandence: QR Code Based Attendance Management System." International Research Journal of Engineering and Technology (IRJET), vol. 7, no. 4, 2020, pp. 745-750.
10. Sharma, R., and Gupta, P. "Developing and Implementing a Barcode Based Student Attendance System." International Research Journal of Engineering and Technology (IRJET), vol. 6, no. 1, 2019, pp. 89-94.
11. Kumar, S., and Raj, P. "Fingerprint Based Attendance Management System." International Journal of Scientific and Engineering Research (IJSER), vol. 10, no. 5, 2019, pp. 215-220.
12. Al-Hajri, R., and Al-Harbi, H. "Using Barcode to Track Student Attendance and Assets in Higher Education Institutions." Procedia Computer Science, vol. 190, 2021, pp. 778-785.
13. Ahmed, T., and Singh, R. "Design and Implementation of a Fingerprint-Based Attendance Management System." International Journal of Computer Science and Mobile Computing (IJCSMC), vol. 8, no. 6, 2019, pp. 22-30.
14. Punekar, Jay. "Face Recognition Attendance System." International Journal of Computer Applications, vol. 182, no. 1, 2021, pp. 45-52.
15. Rohan, G. "Face Recognition-Based Attendance System." International Journal of Emerging Technologies in Learning (iJET), vol. 15, no. 8, 2020, pp. 112-118.
16. Smith, A., et al. "Deep Learning-Based Face Recognition Attendance System." IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 4, 2021, pp. 305-317.
17. Patel, R., et al. "Real-Time Face Recognition Attendance System Using OpenCV and Machine Learning." International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), vol. 10, no. 3, 2020, pp. 98-105.
18. Gupta, S., et al. "AI-Powered Smart Attendance System with Facial Recognition." International Conference on Artificial Intelligence and Machine Learning (ICAIML), 2021, pp. 215-222.