Face Recognition with Python

This paper is based upon the fact that an enormous number of people are using Face Recognition to login into their mobile phones or laptops or in other areas for security reasons. Our aim here is to detect maximizing of the Face Recognition while minimizing the error.

Keywords

Face Recognition Face Detection Haar Cascade Classifier Computer Vision OpenCV Python

References

ace Recognition: Issues, Methods and Alternative Applications By Waldemar Wójcik, Konrad Gromaszek and Muhtar Junisbekov Submitted: October 28th 2015Reviewed: March 9th 2016Published: July 6th 2016 DOI: 10.5772/62950
D. Peleshko and K. Soroka, “Research of usage of Haar-like features and AdaBoost algorithm in Viola-Jones method of object detection,” 2013 12th International Conference on the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), 2013, pp. 284-286.
Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman and A. Y. Wu, “An efficient k-means clustering algorithm: analysis and implementation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, July 2002, doi: 10.1109/TPAMI.2002.1017616.

Download

PDF

How to Cite

Deepika Rawat, Deepanshu, Vishal Gupta, Megha Attri, Abhishek Upadhyay . (2021). Face Recognition with Python. IITM Journal of Management and IT, 12(1`), 24-28.

About This Journal

IIT

IITM Journal of Management and IT

Published by Institute of Information Technology & Management

P-ISSN 2349-9826

✓ Citation copied to clipboard

[1] ace Recognition: Issues, Methods and Alternative Applications By Waldemar Wójcik, Konrad Gromaszek and Muhtar Junisbekov Submitted: October 28th 2015Reviewed: March 9th 2016Published: July 6th 2016 DOI: 10.5772/62950

[2] D. Peleshko and K. Soroka, “Research of usage of Haar-like features and AdaBoost algorithm in Viola-Jones method of object detection,” 2013 12th International Conference on the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), 2013, pp. 284-286.

[3] Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman and A. Y. Wu, “An efficient k-means clustering algorithm: analysis and implementation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, July 2002, doi: 10.1109/TPAMI.2002.1017616.