ARIF: Autonomous Recognition in the Field Enhancing National Security with Computer Vision-Based Facial Recognition
DOI:
https://doi.org/10.54878/z68s0z54Keywords:
biometrics, facial recognition, AI, ML, Computer Vision, Identification, National Security, Security, Homeland Security, Siamese Neural Networks, Convolutional Neural NetworksAbstract
Through a novel research approach that employs a mix of Convolutional Neural architectures & Siamese Neural Nets, we propose a viable mechanism that focuses on leveraging these groundbreaking advancements, through the utilization of deep learning algorithms we were able to effectively & accurately identify and authenticate individuals based on unique facial features derived from machine learnt embeddings. In The ARIF Project we implement the proposed architecture models through utilization of developer friendly modules like the python facial recognition library, the OpenCV framework & Jupiter Notebooks, performing the necessary product development, market research and product analysis throughout the development process, finally deliver a refined & minimalistic solution that not only fills market gaps but also serves as a solid foundation for rapid adoption & deployment.
References
Criminality in Sudan - the organized crime index. The Organized Crime Index | ENACT. (n.d.-a). https://ocindex.net/country/sudan
Crime rate by country 2023. (n.d.-a). https://worldpopulationreview.com/country-rankings/crimerate-by-country
Raposo, Vera Luacutecia. “The Use of Facial Recognition Technology by Law Enforcement in Europe: A Non-Orwellian Draft Proposal - European Journal on Criminal Policy and Research.” SpringerLink, 1 June 2022, link.springer.com/article/10.1007/s10610-022-09512-y
Aware, Inc. (2023, February 22). Facial Recognition & law enforcement – The Value Proposition. https://www.aware.com/blog-facial-recognition-used-in-law-enforcement/
Blog-manager. (2018, October 9). Traditional vs automated security systems in the Philippines: Elid Blog. ELID Technology Intl., Inc. https://elid.com.ph/blog/traditional-vs-automated-security/
O’Shea, Keiron, and Ryan Nash. “An Introduction to Convolutional Neural Networks.” arXiv.Org, 2 Dec. 2015, arxiv.org/abs/1511.08458.
G Koch. "Siamese Neural Networks for One-shot Image Recognition". cs.cmu.edu, , https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf.
Gangarapu, Katam Raju. “Ethics of Facial Recognition: Key Issues and Solutions.” Learn Hub, learn.g2.com/ethics-of-facial-recognition. Accessed 5 Aug. 2023.
Downsizinggovernment.org. (n.d.). https://www.downsizinggovernment.org/homeland-security
Stop retail violence, theft with face matching, video analytics. FaceFirst. (n.d.). https://www.facefirst.com/
Almabdy, S., & Elrefaei, L. (2019, October 17). Deep convolutional neural network-based approaches for face recognition. MDPI. https://www.mdpi.com/2076-3417/9/20/4397
MementoDeAMor. “Mementodeamor/OpenSecurity: An Open-Source Framework for Computer Vision Based Tasks.” GitHub, github.com/MementoDeAMor/OpenSecurity. Accessed 5 Aug. 2023.
“Criminality in Sudan.” The Organized Crime Index, ocindex.net/country/sudan. Accessed 5 Aug. 2023.
Downloads
Received
Accepted
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Automation and Digital Transformation
This work is licensed under a Creative Commons Attribution 4.0 International License.