Student Attendance via Face Recognition
Budget: $10 – $30 USD
1. Problem Analysis and Understanding: (10 Marks)
• Question: Explain the primary goals of implementing a face recognition system
for student attendance via CCTV. Identify potential challenges and ethical
considerations.
• Marking Scheme:
o Clear explanation of objectives: 4 marks
o Identification of technical challenges: 3 marks
o Discussion of ethical considerations: 3 marks
2. Data Collection and Preparation: (15 Marks)
• Question: Detail the process of data collection and preparation for this project.
Include considerations for data quality and privacy.
• Marking Scheme:
o Explanation of data collection process: 5 marks
o Discussion on data preparation (pre-processing, augmentation): 5 marks
o Consideration of data quality and privacy: 5 marks
3. Model Selection and Training: (20 Marks)
• Question: Choose an appropriate machine learning model for face recognition
and justify your choice. Describe the training process.
• Marking Scheme:
o Justification of model choice: 5 marks
o Description of training process: 10 marks
o Explanation of the hyperparameter tuning: 5 marks
4. Model Validation: (15 Marks)
• Question: Explain the validation techniques you would use to assess your
model's performance. Discuss suitable metrics.
• Marking Scheme:
o Explanation of validation techniques: 7 marks
o Discussion of validation metrics: 8 marks
5. System Integration: (15 Marks)
• Question: Describe how you would integrate the trained model with a CCTV feed
to automatically register student attendance.
• Marking Scheme:
o Description of integration process: 8 marks
o Considerations for real-time data handling: 7 marks
6. Documentation and Reporting: (10 Marks)
• Question: Document the project phases and results. Include code excerpts,
diagrams, and lessons learned.
• Marking Scheme:
o Clarity and organization: 4 marks
o Use of supporting materials (like diagrams): 3 marks
o Depth of reflection and conclusions: 3 marks
7. Source Code Submission: (15 Marks)
• Question: Submit the complete source code for the entire project. Ensure that it
is well-documented and organized, demonstrating best practices in coding and
version control.
• Marking Scheme:
o Code structure and organization: 5 marks
o Code readability and documentation (comments, naming conventions): 5
marks
o Proper use of version control tools (e.g., Git): 5 marks
• Question: Explain the primary goals of implementing a face recognition system
for student attendance via CCTV. Identify potential challenges and ethical
considerations.
• Marking Scheme:
o Clear explanation of objectives: 4 marks
o Identification of technical challenges: 3 marks
o Discussion of ethical considerations: 3 marks
2. Data Collection and Preparation: (15 Marks)
• Question: Detail the process of data collection and preparation for this project.
Include considerations for data quality and privacy.
• Marking Scheme:
o Explanation of data collection process: 5 marks
o Discussion on data preparation (pre-processing, augmentation): 5 marks
o Consideration of data quality and privacy: 5 marks
3. Model Selection and Training: (20 Marks)
• Question: Choose an appropriate machine learning model for face recognition
and justify your choice. Describe the training process.
• Marking Scheme:
o Justification of model choice: 5 marks
o Description of training process: 10 marks
o Explanation of the hyperparameter tuning: 5 marks
4. Model Validation: (15 Marks)
• Question: Explain the validation techniques you would use to assess your
model's performance. Discuss suitable metrics.
• Marking Scheme:
o Explanation of validation techniques: 7 marks
o Discussion of validation metrics: 8 marks
5. System Integration: (15 Marks)
• Question: Describe how you would integrate the trained model with a CCTV feed
to automatically register student attendance.
• Marking Scheme:
o Description of integration process: 8 marks
o Considerations for real-time data handling: 7 marks
6. Documentation and Reporting: (10 Marks)
• Question: Document the project phases and results. Include code excerpts,
diagrams, and lessons learned.
• Marking Scheme:
o Clarity and organization: 4 marks
o Use of supporting materials (like diagrams): 3 marks
o Depth of reflection and conclusions: 3 marks
7. Source Code Submission: (15 Marks)
• Question: Submit the complete source code for the entire project. Ensure that it
is well-documented and organized, demonstrating best practices in coding and
version control.
• Marking Scheme:
o Code structure and organization: 5 marks
o Code readability and documentation (comments, naming conventions): 5
marks
o Proper use of version control tools (e.g., Git): 5 marks