DEVELOPING ADVANCED FACIAL RECOGNITION SOFTWARE USING VIDEO CAMERAS: TECHNIQUES AND APPLICATIONS

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Published: Jun 14, 2024
Keywords:
Keywords: Facial Recognition, Biometric Technology, Image Processing, Feature Extraction, Video Surveillance, Real-Time Identification, Algorithm Development, Security Systems, Grayscale Conversion, Pattern Recognition, Database Storage, Dynamic Characteristics, Software Development, Facial Features.

Main Article Content

Hamiyev A.T.
Kholiyorov Kh.A.

Abstract

This article explores the development of advanced facial recognition software using video cameras. The focus is on the image processing techniques and biometric technologies employed to enhance facial recognition accuracy. Key methodologies include feature extraction, encoding, and image segmentation, which are essential for identifying and analyzing facial features. The thesis also discusses the creation and implementation of robust algorithms for real-time detection and recognition, emphasizing the software's practical applications in security systems. This comprehensive study highlights the potential of integrating facial recognition technology into various real-world scenarios, offering significant improvements in security and efficiency.

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How to Cite
Hamiyev A.T., & Kholiyorov Kh.A. (2024). DEVELOPING ADVANCED FACIAL RECOGNITION SOFTWARE USING VIDEO CAMERAS: TECHNIQUES AND APPLICATIONS. World Scientific Research Journal, 28(1), 162–166. Retrieved from http://wsrjournal.com/index.php/wsrj/article/view/3327
Issue
Vol. 28 No. 1 (2024): WORLD SCIENTIFIC RESEARCH JOURNAL | WSRJournal | WSRJ | www.wsrjournal.com | Volume-28 | Issue-1
Section
Статьи

References

Jain, A. K., Ross, A., & Prabhakar, S. (2004). An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4-20.

Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face Recognition: A Literature Survey. ACM Computing Surveys (CSUR), 35(4), 399-458.

Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71-86.

Hamiyev A.T., Saidov M.M. Comparative analysis of image segmentation algorithms. Collection of reports International scientific and practical conference “Role of digital technologies in economy and education” April 26-27, 2024. Samarkand, Uzbekistan, 338-341.

Bekmurodov Q.A., Hamiyev A.T., Fayziev V.O., Mamatqulov M. Konvolutsion neyron tarmoqlari. Collection of reports International scientific and practical conference “Role of digital technologies in economy and education” April 26-27, 2024. Samarkand, Uzbekistan, 324-327.

Viola, P., & Jones, M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 1, 511-518.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 770-778.

Daugman, J. (2009). How Iris Recognition Works. Handbook of Biometrics, 351-370.

Brunelli, R., & Poggio, T. (1993). Face Recognition: Features versus Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10), 1042-1052.

Bledsoe, W. W. (1966). The Model Method in Facial Recognition. Panoramic Research Inc..

Prince, S. J. D. (2012). Computer Vision: Models, Learning, and Inference. Cambridge University Press.

Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep Face Recognition. Proceedings of the British Machine Vision Conference (BMVC 2015).

Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), 815-823.

Hjelmås, E., & Low, B. K. (2001). Face Detection: A Survey. Computer Vision and Image Understanding, 83(3), 236-274.