Conference paper · 2023
Experimental Face Recognition Using Applied Deep Learning Approaches to Find Missing Persons
Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021, Lecture Notes in Networks and Systems, vol. 404, pp. 3–11, Springer Nature Singapore · Published
Research summary
The summary, key findings, methodology and relevance notes below are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.
- The Problem
- More than 23,000 people were reported missing in Nigeria in 2020, and existing search methods — word of mouth, media announcements and social media — are slow and ineffective.
- The Methodology
- Experimental system design: face recognition with a convolutional neural network, facial calibration and modelling for feature extraction, and comparison of face encodings against a query image.
- The Core Finding
- The authors built an experimental face-recognition system that combines a convolutional neural network with facial calibration and modelling to extract face encodings and match them against a query image, as a faster tool for identifying missing persons.
- The Citation
- Imoh, N., Vajjhala, N. R., & Rakshit, S. (2023). Experimental Face Recognition Using Applied Deep Learning Approaches to Find Missing Persons. In Subhadip Basu, Dipak Kumar Kole, Arnab Kumar Maji, Dariusz Plewczynski, Debotosh Bhattacharjee (Eds.), Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021 (pp. 3–11). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0105-8_1
What question does this paper answer?
Can a facial recognition system with deep learning functionality help law enforcement agencies, human rights organisations and families find and identify missing persons in Nigeria faster than word of mouth, media announcements and social media?
What did the study find, in detail?
The authors build an experimental system that combines facial recognition with a convolutional neural network. High-standard facial calibration and modelling are used for feature extraction; the extracted features form face encodings that are then compared with a given image to identify a match. The paper motivates the system with ICRC figures of more than 40,000 people reported missing in Africa in 2020, over 23,000 of them in Nigeria, most attributed to insurgency and insecurity.
Why does it matter?
Current approaches to finding missing persons in Nigeria are described as slow and ineffective where time is critical. An automated, deep-learning-based face matching tool could speed up search and identification for law enforcement, human rights organisations and families.
Key findings
- According to the ICRC (2020), more than 40,000 people were declared missing in Africa, a little over 23,000 of them in Nigeria; most Nigerian cases are attributed to the insurgency and security crisis of the preceding decade.
- Existing solutions in Nigeria — word of mouth, media and print announcements and, more recently, social media — are described as inefficacious and slow, particularly when time is a determining factor.
- The experimental system combines facial recognition with deep learning using a convolutional neural network, with high-standard facial calibration and modelling used for feature extraction.
- Extracted features form face encodings that are compared against a given image to identify a person.
Source: Imoh et al. (2023), Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021, Lecture Notes in Networks and Systems, vol. 404, pp. 3–11, Springer Nature Singapore. DOI: 10.1007/978-981-19-0105-8_1
Study at a glance
| Problem | Over 40,000 people declared missing in Africa in 2020 (ICRC), more than 23,000 in Nigeria; current search methods are slow and inefficacious |
|---|---|
| Design | Experimental face recognition system |
| Methods | Deep learning with a convolutional neural network; high-standard facial calibration and modelling for feature extraction; face encodings compared with a given image |
| Intended users | Nigerian law enforcement agencies, human rights organisations, friends and families of missing persons |
| Implication | Deep learning face matching could speed up the search-and-find process where time is decisive |
| Citation | Imoh et al. (2023) · DOI 10.1007/978-981-19-0105-8_1 |
Abstract
The spike in challenges to security as well as information and resource management across the globe has equally borne the rising demand for a better system and technology to curb it. A news release from the International Committee of the Red Cross (ICRC) in 2020 revealed over 40,000 people were declared missing in Africa. A staggering percentage of that number, a little over 23,000, is documented in Nigeria alone. Despite the numerous factors surrounding missing persons globally, at more than 50% of the original figure, it is unsurprising that most of the cases in Nigeria are attributed to the insurgency and security mishap that has plagued the country for almost a decade. Some of the cases remain unsolved for years, causing the victims to remain untraceable, thereby taking up a different identity and existence, especially if they went missing. Current solutions to find missing persons in Nigeria revolve around word of mouth, media and print announcements, and more recently, social media. These solutions are inefficacious, slow, and do not adequately help find and identify missing persons, especially in situations where time is a determining factor. The use of a facial recognition system with deep learning functionality can help Nigerian law enforcement agencies, and other human rights organizations and friends and families of the missing person speed up the search and find process. Our experimental system combines facial recognition with deep learning using a convoluted neural network. In this study, the authors have used high-standard facial calibration and modeling for feature extraction. These extracted features form the face encodings that are after that compared to a given image.
Abstract as published in Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021.
Keywords: Deep learning; Face recognition; Artificial intelligence; Neural network; Machine learning; Convolutional neural networks
Limitations
- The abstract describes an experimental system and its design; it does not report accuracy figures, dataset size or field deployment results.
When this research may be relevant
This paper may be relevant to researchers studying face recognition with convolutional neural networks, humanitarian and law-enforcement applications of computer vision, missing-person identification systems, and technology responses to insecurity and displacement in Nigeria and sub-Saharan Africa.
Research topics addressed: face recognition; deep learning; convolutional neural networks; missing persons; computer vision; facial feature extraction; face encodings; law enforcement technology; Nigeria; humanitarian technology
How to cite
Imoh, N., Vajjhala, N. R., & Rakshit, S. (2023). Experimental Face Recognition Using Applied Deep Learning Approaches to Find Missing Persons. In Subhadip Basu, Dipak Kumar Kole, Arnab Kumar Maji, Dariusz Plewczynski, Debotosh Bhattacharjee (Eds.), Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021 (pp. 3–11). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0105-8_1
BibTeX
@inproceedings{imoh2023deep,
title = {Experimental Face Recognition Using Applied Deep Learning Approaches to Find Missing Persons},
author = {Imoh, Nsikak and Vajjhala, Narasimha Rao and Rakshit, Sandip},
booktitle = {Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021},
series = {Lecture Notes in Networks and Systems},
editor = {Subhadip Basu and Dipak Kumar Kole and Arnab Kumar Maji and Dariusz Plewczynski and Debotosh Bhattacharjee},
pages = {3--11},
year = {2023},
publisher = {Springer Nature Singapore},
doi = {10.1007/978-981-19-0105-8_1},
url = {https://doi.org/10.1007/978-981-19-0105-8_1}
}Related research