Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Toward Digitalization: A Secure Approach to Find a Missing Person Using Facial Recognition Technology (2405.16683v1)

Published 26 May 2024 in cs.CV, cs.CY, and cs.LG

Abstract: Facial Recognition is a technique, based on machine learning technology that can recognize a human being analyzing his facial profile, and is applied in solving various types of realworld problems nowadays. In this paper, a common real-world problem, finding a missing person has been solved in a secure and effective way with the help of facial recognition technology. Although there exist a few works on solving the problem, the proposed work is unique with respect to its security, design, and feasibility. Impeding intruders in participating in the processes and giving importance to both finders and family members of a missing person are two of the major features of this work. The proofs of the works of our system in finding a missing person have been described in the result section of the paper. The advantages that our system provides over the other existing systems can be realized from the comparisons, described in the result summary section of the paper. The work is capable of providing a worthy solution to find a missing person on the digital platform.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (12)
  1. S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian, Y. Tao, M. P. Reyes, M.-L. Shyu, S.-C. Chen, and S. Iyengar, “A survey on deep learning: Algorithms, techniques, and applications,” ACM Computing Surveys (CSUR), vol. 51, no. 5, pp. 1–36, 2018.
  2. D. Özkan, “A graph based approach for finding people in news,” Ph.D. dissertation, bilkent university, 2007.
  3. D. Deb, D. Aggarwal, and A. K. Jain, “Finding missing children: Aging deep face features,” arXiv preprint arXiv:1911.07538, 2019.
  4. R. Natsume, T. Yatagawa, and S. Morishima, “Fsnet: An identity-aware generative model for image-based face swapping,” in Asian Conference on Computer Vision.   Springer, 2018, pp. 117–132.
  5. A. Raghuwanshi and P. D. Swami, “An automated classroom attendance system using video based face recognition,” in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).   IEEE, 2017, pp. 719–724.
  6. A. Nech and I. Kemelmacher-Shlizerman, “Level playing field for million scale face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7044–7053.
  7. X. Jin, S. Ge, C. Song, X. Li, J. Lei, C. Wu, and H. Yu, “Double-blinded finder: A two-side privacy-preserving approach for finding missing children,” in 3rd EAI International Conference on Robotic Sensor Networks.   Springer, 2020, pp. 33–43.
  8. C. M. Bharath Darshan Balar, D S Kavya, “Efficient face recognition system for identifying lost people,” International Journal of Engineering and Advanced Technology (IJEAT), 2019.
  9. P. Muyambo, “An investigation on the use of lbph algorithm for face recognition to find missing people in zimbabwe,” International Journal of Engineering and Advanced Technology (IJEAT), 2018.
  10. “face-recognition 1.3.0,” 2021, last accessed on June 2, 2021, at 11:24:00AM. [Online]. [Online]. Available: https://pypi.org/project/face-recognition/
  11. M. Mamun, S. Alam, M. Hossain, and M. Samiruzzaman, “A novel approach to blockchain-based digital identity system,” in Advances in Intelligent Systems and Computing, vol. 1129.   Springer, 2020, pp. 93–112.
  12. M. van Steen and A. Tanenbaum, “A brief introduction to distributed systems,” vol. 98, p. 967–1009, 2016.

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com