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Sudan Local Chapter – Identification Services with Machine Learning
Presently in Sudan, there is no standardised Civilian Identification Record despite the rising crime rates this nation experiences, this reality adversely hinders the process of Identifying Personnel and impairs both the pursuit of Justice and hinders the process of Personnel Authentication and Verification. It is our firm belief that the introduction of this technology would promote the introduction and adoption of a standardised personnel Identification system and greatly improve national security standards. Such a system would help keep track of civilian identities, as well as provide easy and ubiquitous access to this system. The same framework could also be replicated or implemented on different scales for private Identification services and for creating complex ID based security systems.
Sudan Experiences a lot of crime and despite the rising crime rate, it still lacks a standardised Digital Civilian Record System. Completely reliant on paper based authentication, the current system is both resource exhaustive and time consuming. Taking days in pursuit of paper trails, where it would take seconds digitally. The process of Record Retrieval, Personnel verification, background checks and clearing are all done manually and based on paper trail based approaches. In this solution we propose the use of a cloud based digital identification system that uses a relational database system and relies on Machine Learning to Identify, Assess, Examine and manage security operations, allowing for the creation and deployment of an easy to use system for Identification and RecordKeeping.
THE PROJECT GOALS:
Identification Services with Machine Learning
- To develop a recognition model that will identify faces with a high degree of accuracy.
- To Test our Models accuracy and attempt to improve on our model.
- To integrate our final model into a suitable Database, in an application.
- To deploy an API or demo of the proposed system.
- To Test the final Product. Measure its effectiveness.
THE LEARNING OUTCOMES:
- 1. Data collection
- 2. Data Processing
- 3. Labelling of Data
- 4. ML Model for extraction of Face.
- 5. ML Model for identification and comparison of Faces against Known Database.
- 6. A Database system for storing the Face-Recognition Database Content.
- 7. Testing of Results and Fine Tuning the model.
- 8. Deployment of the whole system
THE TASKS & TIMELINE:
2.Understanding the problem
2.Pre-processing and analysis.
1.Understanding the Model.
2. Deeper Into the ML models
1.Implementing the Model.
2.Fine-Tuning The Model.
1.Building the Database.
2.Integrating the Database.
3.Deploying the Model.
Sudan Chapter Lead
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