Digital Criminal Biometric Archives (DICA) and Public Facial Recognition System (FRS) for Nigerian criminal investigation using HAAR cascades classifier technique

Onyemachi Joshua Ndubuisi,; Gift Adene,; Belonwu Tochukwu Sunday,; Chinedu Emmanuel Mbonu,; Adannaya Uneke Gift-Adene,

doi:10.30574/wjaets.2024.11.2.0077

Cited by 1 publication

(1 citation statement)

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“…The most crucial responsibility for the police is criminal identification since it requires them to look everywhere for it [1]. Law enforcement organizations play a crucial role in maintaining peace, order, and security [2].…”

Section: Introductionmentioning

confidence: 99%

Digitally improving UK police surveillance and incidence response using real-time crowd reporting app: Digipolice

Onyemachi Joshua Ndubuisi,

Gift Adene,

Belonwu Tochukwu Sunday

et al. 2024

Global J. Eng. Technol. Adv.

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Overtime, evolving technologies has made ways of monitoring and controlling crime and action in the United Kingdom (UK) become more challenging which needs optimization with the help of technologies. Also with the rate of crime across cities in the UK like mass shootings, murder and gang gun battles, the UK Police system within localities are overwhelmed or may not efficiently cope with crime alerts and incidences coming in at increasing frequencies. However looking in the area of real time crowd reporting, there is need to leverage technology to enhance public safety, improve incident response, and foster community engagement. This necessitated the aim of this study to develop a digital policing surveillance app for crowd reporting in the UK. By utilizing digital policing surveillance apps, the aim is to improve crowd management capabilities by enabling real-time reporting of incidents and observations from both the public and law enforcement personnel. This was positioned to facilitate better situational awareness, efficient resource allocation, and effective response strategies. To achieve the objectives, Python Programming Language 3.8 were deployed together with a Django Framework and a Microsoft Visual Studio Build Tools. In the DP system, the Model-View-Controller (MVC) pattern, which divides an application into three interconnected components—the model, the view, and the controller—was used for the architectural design of the system. For the database, Django's ORM makes it simple to support many databases, and in this project straightforward SQLite3 database were utilised which is ideal for small-footprint applications. Using WebSockets to establish real-time, bidirectional communication between web clients and servers, the user interface and intelligence board for the system were developed. A clean designed frontend design system with Bootstrap version 5—an open-source, reusable design framework created by Twitter—was selected for its easy user interface. The system was put through 50 concurrent load tests with actual users swarming the app since the DP app was subjected to hundreds of people complaining in real time. 2. The host computer had a Core i5 vPro CPU running at 4GHz and 4GB of RAM. The result shows that 50 users successfully generated an average of 29.7 requests per second. The system scaled up nicely to 50 users and reached its highest response time of 2600 milliseconds (2.6 seconds) in total. A dramatic rise in activity and a longer response time occurred after there were more than 50 users. The system could not operate at its peak efficiency with 100 users because response times were inconsistent and exceeded 7000 milliseconds (7 seconds) for each request. The effectiveness of the online deployment was evaluated with respectable results, and the system requirements also underwent successful testing. The appropriate investigation of ethical issues was done to meet requirements. DigiPolice has the potential to develop into a cutting-edge programme for real-time crowd surveillance and incident reporting. UK Police can employ it to as advancement to its “My Police” with as fast-track real-time and collaborative crowd reporting enhancements.

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Section: Introductionmentioning

confidence: 99%