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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64271

AI BASED FACE RECOGNITION & DETECTION

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64271

Volume:

10 |

5 |

May 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VP

Vishwajeet Pawar

Artificial Intelligence and Dtat Science

Suman Ramesh Tulsiani tecnical campus faculty of engineering

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YB

Yuvraj Bhore

Artificial Intelligence and Dtat Science

Suman Ramesh Tulsiani tecnical campus faculty of engineering

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BB

B.N Babar

Artificial Intelligence and Dtat Science

Suman Ramesh Tulsiani tecnical campus faculty of engineering

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GS

Gaurav Surwase

Artificial Intelligence and Dtat Science

Suman Ramesh Tulsiani tecnical campus faculty of engineering

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SD

Swaraj Darekar

Artificial Intelligence and Dtat Science

Suman Ramesh Tulsiani tecnical campus faculty of engineering

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Mentors

BB

B.N Babar

Artificial Intelligence and Dtat Science

Suman Ramesh Tulsiani tecnical campus faculty of engineering

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Abstract

The ability of machines to interpret and process visual data has greatly improved in recent years due to the quick development of deep learning and artificial intelligence (AI). The field of face recognition and detection systems is one of the most significant applications of this advancement. By accurately and efficiently analyzing facial features from photos or video streams, these systems allow machines to identify and verify people. The design and development of an AI-based face recognition and detection system that makes use of cutting-edge deep learning methods, specifically Convolutional Neural Networks (CNNs), is presented in this project. In order to identify people, the system can recognize faces in real time and compare them to a database that already exists. High performance in terms of speed, accuracy, and adaptability under various environmental conditions is the goal of the suggested solution. Security surveillance, automated attendance systems, biometric authentication, and missing person identification are just a few of the many uses for such systems. Notwithstanding its benefits, the technology also brings with it drawbacks like privacy issues, moral dilemmas, and sensitivity to lighting and occlusion. The methodology, applications, benefits, drawbacks, and potential advancements of the suggested system are all thoroughly covered in this paper.

KEYWORDS

Convolutional Neural Networks (CNNs) Artificial Intelligence Computer Vision and Machine Learning.

Cite This Article

Vishwajeet Pawar, Yuvraj Bhore, B.N Babar, Gaurav Surwase, Swaraj Darekar, B.N Babar, "AI BASED FACE RECOGNITION & DETECTION," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-6, May 2026.

Full Article/Conclusion

The face recognition system that uses intelligence is a big step forward in automation. It uses learning methods to quickly and correctly identify people in real time.   This system can be used in areas like security, healthcare, finance and consumer technology which shows how important it is in our daily lives. There are some problems like privacy concerns and limitations because of the environment. People are working on these issues and technology is getting better.   If we use face recognition technology in a way and keep making it better it can really change the way we identify people and make our surroundings safer and more efficient. Face recognition technology has a lot of potential to make a difference, in our lives with face recognition technology.

References

·        S. Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2nd Edition, 2011. ·        M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991. ·        P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001. ·        G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,” University of Massachusetts, Amherst, 2007. ·         K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detection and Alignment Using Multi-task Cascaded Convolutional Networks,” IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499–1503, 2016. ·        I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. ·        Schroff, F., Kalenichenko, D., and Philbin, J., “FaceNet: A Unified Embedding for Face Recognition and Clustering,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ·        Taigman, Y., Yang, M., Ranzato, M., and Wolf, L., “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. ·        Parkhi, O. M., Vedaldi, A., and Zisserman, A., “Deep Face Recognition,” British Machine Vision Conference (BMVC), 2015.

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Publication Date

5/29/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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