Saurabh Shandilya,
Shubham Prajapati,
Vishal Agrawal,
Rahul Arora,
Sohaib Nasir,
- Professor, Department of Computer Engineering Poornima College of Engineering Jaipur, Rajasthan, India
- Student, Department of Computer Engineering Poornima College of Engineering Jaipur, Rajasthan, India
- Student, Department of Computer Engineering Poornima College of Engineering Jaipur, Rajasthan, India
- Student, Department of Computer Engineering Poornima College of Engineering Jaipur, Rajasthan, India
- Student, Department of Computer Engineering Poornima College of Engineering Jaipur, Rajasthan, India
Abstract
These days, one of the biggest computer vision technologies is facial recognition. Face identification in computer vision, lighting position, and facial expression is always an extremely challenging issue. In real-time video pictures captured by a video camera, face recognition tracks specific objects. Put simply, it is a system tool that uses a still picture or video frame to automatically identify a person. In this research paper we use different different algorithm for face recognition like Viola-Jomes algorithm, PCA algorithm, KLT algorithm and Emgu CV. We can carry through the aforementioned procedures using a variety of strategies. The present work here depicts the details of various technologies which have been researched and discovered. Let us take an example of designing color filters that we use in cameras to make them more colorimetric.
Keywords: Face recognition, PCA, viola-JOMES, Dual mode QR codes, video anomaly detection
[This article belongs to Trends in Machine design (tmd)]
Saurabh Shandilya, Shubham Prajapati, Vishal Agrawal, Rahul Arora, Sohaib Nasir. AI-Powered Face Detection and Recognition Using Machine Learning. Trends in Machine design. 2024; 11(02):1-12.
Saurabh Shandilya, Shubham Prajapati, Vishal Agrawal, Rahul Arora, Sohaib Nasir. AI-Powered Face Detection and Recognition Using Machine Learning. Trends in Machine design. 2024; 11(02):1-12. Available from: https://journals.stmjournals.com/tmd/article=2024/view=176553
References
1. Barron I, Hsin Jui Yeh, Karthik Dinesh, Sharma G. Dual Modulated QR Codes for Proximal Privacy and Security. IEEE Transactions on Image Processing . 2021;30:657–69. 2. Graham D. Finlayson, Member, IEEE, and Yuteng Zhu. Designing Color Filters That Make Cameras More Colorimetric. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 30, 2021. 853-867. 3. Wu P, Liu J. Learning Causal Temporal Relation and Feature Discrimination for Anomaly Detection. IEEE Transactions on Image Processing . 2021;30:3513–27. 4. Sahu A, Chowdhury AS. Together Recognizing, Localizing and Summarizing Actions in Egocentric Videos. IEEE Transactions on Image Processing .2021;30:4330–40. 5. Gu C, Lu X, He Y, Zhang C. Blur Removal Via Blurred-Noisy Image Pair. IEEE Transactions on Image Processing . 2021;30:345–59. 6. Li YH, Lo I-Chan, Chen HH. Deep Face Rectification for 360° Dual-Fisheye Cameras. IEEE Transactions on Image Processing. 2021 ;30:264–76. 7. Guo Y, Cai L, Zhang J. 3D Face From X: Learning Face Shape From Diverse Sources. IEEE Transactions on Image Processing . 2021 ;30:3815–27. 8. Kasar MM, Bhattacharyya D, Kim T. Face Recognition Using Neural Network: A Review. International Journal of Security and Its Applications. 2016;10(3):81–100. 9. Liu Z, Wang Y. Face detection and tracking in video using dynamic programming. 2002 ; Vancouver, BC, Canada. DOI: 10.1109/ICIP.2000.900890. 10. Ritesh Boda, M Jasmine Pemeena Priyadarsini, J Pemeena. 11. Turk M, Pentland A. Eigenfaces for Recognition. Journal of Cognitive Neuroscience. 1991;3(1):71–86. 12. Beymer D, Poggio T. Face recognition from one example view. Proceedings of IEEE International Conference on Computer Vision. 2002; Cambridge, MA, USA. DOI: 10.1109/ICCV.1995.466898. 13. Wu YH, Gao SH, Mei J, Xu J, Fan DP, Zhang RG, et al. JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation. IEEE Transactions on Image Processing. 2021 ;30:3113–26. 14. Wang Y, Yang J, Wang L, Ying X, Wu T, An W, et al. Light Field Image Super-Resolution Using Deformable Convolution. IEEE Transactions on Image Processing . 2021;30:1057–71. 15. Yang H, Gong C, Huang K, Song K, Yin Z. Weighted Feature Histogram of Multi-Scale Local Patch Using Multi-Bit Binary Descriptor for Face Recognition. IEEE Transactions on Image Processing. 2021;30:3858–71.
Trends in Machine design
| Volume | 11 |
| Issue | 02 |
| Received | 30/05/2024 |
| Accepted | 06/08/2024 |
| Published | 01/10/2024 |