Add Face Recognition System Proposal and Project Plan #31
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| - Setup all the Django API Endpoints related to image processing and storing, and MongoDB Database | ||
| - Setup Django Server and handle API Endpoint up to image encoding | ||
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| **Example Code Snippet:** | ||
| ```python | ||
| # Assume 'image_data' is the Base64-encoded image sent as a POST request to the endpoint | ||
| image_data = request.POST.get('image') | ||
| # Decode the Base64-encoded image data to a NumPy array | ||
| img_data = base64.b64decode(image_data) | ||
| image = Image.open(io.BytesIO(img_data)) | ||
| # after this point we’ll take over | ||
| # Save the image to the temp_db directory | ||
| filename = f'tm_system_{i}_image.jpg' | ||
| filepath = os.path.join(temp_db_dir, filename) | ||
| image.save(filepath) | ||
| ``` | ||
| #### Explanation: | ||
| 1. **Endpoint Communication:** | ||
| - The TM systems team is responsible for capturing the image and sending the Base64-encoded image data as a POST request to the designated endpoint. | ||
| 2. **Image Decoding:** | ||
| - The Django server (our team's responsibility) receives the POST request and decodes the Base64-encoded image data using the provided example snippet. | ||
| 3. **Image Processing and Storage:** | ||
| - Our team takes over from the point of image decoding. | ||
| - Further image processing, storage, and any additional tasks are handled by our team. | ||
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| **Communication:** | ||
| Weekly communication should be scheduled to take updates and maintain transparency. We look forward to a successful collaboration and the timely completion of Phase-I. | ||
| ### Phase-II: Prototype Implementation | ||
| #### Tasks from Our End: | ||
| - Setup all API endpoints using FastAPI for image processing and storing | ||
| - Setup and use the MongoDB Database | ||
| - Extensive Testing | ||
| - Modifying the backend code according to frontend setup | ||
| - Changing various functionalities in backend to support multiple images for a person | ||
| - Integrating collected database in MongoDB and migrate from local to MongoDB Atlas | ||
| - Setting up Vector Search in MongoDB database | ||
| - Utility function for efficient vector search | ||
| - New Endpoint for recognizing the face | ||
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| #### Tasks required from T.M Systems: | ||
| - Update the frontend code to support collection of multiple images for a single user | ||
| - Add frontend interface for testing of Vector Search using the API endpoint provided | ||
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| ### Phase-III: Training the Model | ||
| This phase is pivotal for the project, focusing on creating a robust and effective face recognition model through meticulous training and validation processes. | ||
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| #### Key Tasks and Innovations: | ||
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| 1. **Triplet Loss Function Implementation**: | ||
| - Utilizing Triplet Loss as the cornerstone of our model to enhance the accuracy in distinguishing between different individuals. | ||
| - This involves crafting a training strategy that effectively verifies the similarity between the vector embeddings of faces. | ||
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| 2. **Support for Multiple Face Embeddings**: | ||
| - Developing a system architecture capable of managing multiple embeddings per employee to boost recognition precision. | ||
| - This will involve refining data models and possibly the vector search mechanism to handle composite embeddings data. | ||
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| 3. **Extended Test Coverage**: | ||
| - The goal is to extend test coverage to 100% for all new and previously created endpoints, up from the current 86%. | ||
| - This ensures robustness and reliability across all functionalities of the Face Recognition System. | ||
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| 4. **Local MongoDB Atlas Setup and Integration**: | ||
| - Collaborating with the TM systems team for a local MongoDB Atlas setup and ensuring seamless integration with the project. | ||
| - This also involves establishing a coherent system that supports both MongoDB and MongoDB Atlas, enhancing data management and scalability. | ||
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| 5. **Dockerization**: | ||
| - Creating a dockerized version of the project and database for ease of deployment, scalability, and maintenance. | ||
| - Dockerization will aid in ensuring compatibility and efficiency across different development and production environments. | ||
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| 6. **Interoperability between MongoDB and MongoDB Atlas**: | ||
| - Ensuring a seamless flow of operations between local and cloud-based databases to optimize performance and accessibility. | ||
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| #### Additional Considerations: | ||
| - **Data Pipeline Optimization**: Streamlining the data pipeline for efficient training and validation cycles. | ||
| - **Resource Allocation for Training**: Ensuring adequate computational resources are available for the training phase, including GPU specifications and cloud instances. | ||
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| ### Deliverables for Phase-III: | ||
| - A fully trained and validated face recognition model utilizing Triplet Loss. | ||
| - An expanded and optimized backend to support multiple embeddings per employee with robust API endpoints. | ||
| - Increased test coverage ensuring the reliability of all system components. | ||
| - A dockerized version of the project for streamlined deployment and maintenance. | ||
| - Enhanced data management systems with seamless MongoDB and MongoDB Atlas interoperability. | ||
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| ### Future Phases Deliverables | ||
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| Following the successful completion of Phase-III, the project will proceed to subsequent phases focusing on optimization, deployment, and scalability. | ||
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| #### Phase-IV: Implementing Optimization Techniques | ||
| - **Deliverables**: Enhanced system performance through optimized algorithms and codebase refinements. This phase will also focus on minimizing latency and maximizing efficiency in face recognition tasks. | ||
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| #### Phase-V: Deployment | ||
| - **Deliverables**: Deployment of the face recognition system in a real-world environment with complete backend and frontend integration. This includes rigorous testing on a wider scale to validate the model's performance. | ||
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| #### Tasks required from T.M Systems: | ||
| - Integrate the API endpoint with frontend | ||
| - Elaborate testing on your employees to validate the model’s performance | ||
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| #### Phase-VI: Scaling the System | ||
| - **Deliverables**: A strategic plan for scaling the system to accommodate a growing number of users and data. This will involve scalable cloud services, database expansion plans, and computational resource management. | ||
| - This phase involves planning for how to scale the FaceRec System if it is successful. This includes considering factors such as the number of users, the amount of data, and the computational requirements. | ||
| - The deadline for this phase is June. |
There was a problem hiding this comment.
The proposal is well laid out and covers a broad scope of work across different phases, which is great for understanding the project's direction. However, for improvement:
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Security Concerns: When dealing with personal data, especially images for face recognition, it's crucial to mention and plan for security measures and GDPR compliance to protect user data.
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Code Snippet Improvement: The provided example for decoding the Base64-encoded image could be improved by adding error handling to manage cases where the decoding process fails, which ensures the robustness of the endpoint.
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Testing Strategy: While increased test coverage is mentioned, specifying a strategy or framework for testing, particularly for face recognition accuracy and edge cases, would strengthen the proposal.
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Data Preprocessing: It's good practice to include more details on the preprocessing steps for images, such as normalization or augmentation techniques, to enhance model performance.
Example for code snippet improvement with error handling:
try:
img_data = base64.b64decode(image_data)
image = Image.open(io.BytesIO(img_data))
# Proceed with further processing
except (TypeError, ValueError) as e:
# Handle decoding error or invalid data
print("Decoding failed:", e)Refining these areas would address potential roadblocks ahead and showcase a comprehensive plan for successfully implementing the face recognition system.
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This pull request adds the Face Recognition System Proposal and the Project Plan for Phase-I: Collection of Dataset and Requirement Setup.