i-manager Publications

Tracking Missing Persons using Facial Recognition

Bhavika Gupta*, Palak Agarwal**, Disha Devalia***

*-*** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, Maharashtra, India.

Periodicity:January - June'2023
DOI : https://doi.org/10.26634/jpr.10.1.19435

Abstract

Finding missing people is a time-critical and labor-intensive task and the longer it takes to locate the person, the lower the likelihood of a successful outcome. To address this challenge, an integrated and centralized database of missing persons using Aadhar card details was developed. The approach incorporates facial recognition technology, specifically the deep face algorithm, which has shown high accuracy in identifying individuals. While facial recognition has been in use for several years, recent advancements have made it easier to identify individuals accurately. By leveraging Artificial Intelligence (AI) powered facial recognition technology, officials can enhance and streamline the process of finding, tracking, and retrieving missing persons. The system matches facial features with the data stored in Aadhar cards, providing a reliable means of identification. This research presents a system that centralizes data, improving the efficiency of locating missing individuals. By utilizing facial recognition and centralizing data, the system offers an efficient approach to find missing people. The integration of technology and data allows quick and more accurate identification, increasing the chances of locating missing persons promptly.

Keywords

Face Recognition, Face Detection, Deep Face, Tracking and Retrieving.

How to Cite this Article?

Gupta, B., Agarwal, P., and Devalia, D. (2023). Tracking Missing Persons using Facial Recognition. i-manager’s Journal on Pattern Recognition, 10(1), 25-33. https://doi.org/10.26634/jpr.10.1.19435

References

[1]. Dass, R., Rani, R., & Kumar, D. (2012). Face recognition techniques: A review. International Journal of Engineering Research and Development, 4(7), 70-78.

[2]. Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4690-4699).

[3]. Gholape, N., Gour, A., & Mourya, S. (2021). Finding missing person using ML, AI. International Research Journal of Modernization in Engineering Technology and Science, 3, 1517-1520.

[4]. Goyal, K., Agarwal, K., & Kumar, R. (2017, April). Face detection and tracking: Using OpenCV. In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), 1, 474-478. IEEE.

[5]. King, D. E. (2009). Dlib-ml: A machine learning toolkit. The Journal of Machine Learning Research, 10, 1755- 1758.

[6]. Mehta, J., Ramnani, E., & Singh, S. (2018, February). Face detection and tagging using deep learning. In 2018 International Conference on Computer, Communication, and Signal Processing (ICCCSP) (pp. 1-6). IEEE.

[7]. Patil, S., Gaikar, P., Kare, D., & Pawar, S. (2021). Finding missing person using AI. International Journal of Progressive Research in Science and Engineering, 2(6), 101-104.

[8]. Ren, X., Ding, J., Sun, J., & Sui, Q. (2017, October). Face modeling process based on Dlib. In 2017 Chinese Automation Congress (CAC) (pp. 1969-1972). IEEE.

[9]. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 815-823).

[10]. Shahsavarani, S., Analoui, M., & Ghiass, R. S. (2020). M^ 2 Deep-ID: A novel model for multi-view face identification using convolutional deep neural networks. arXiv preprint arXiv:2001.07871.

[11]. Sharma, S., Shanmugasundaram, K., & Ramasamy, S. K. (2016, May). FAREC-CNN based efficient face recognition technique using Dlib. In 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) (pp. 192-195). IEEE.

[12]. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1701-1708).

[13]. Wang, M., & Deng, W. (2021). Deep face recognition: A survey. Neurocomputing, 429, 215-244.

[14]. Zhao, F., Li, J., Zhang, L., Li, Z., & Na, S. G. (2020). Multi-view face recognition using deep neural networks. Future Generation Computer Systems, 111, 375-380.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.