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This project focuses on the backend database engineering required for a high-traffic Face Recognition Attendance System.
Instead of focusing on the AI model, this repo demonstrates:
- Schema Design: Normalized tables for students and logs.
- Data Simulation: Python scripts to generate 100,000+ realistic attendance records.
- Performance Tuning: Demonstrating how Composite Indexes reduce query time from massive scans to instant lookups.
- Students: Stores identity and vector placeholders.
- AttendanceLogs: High-volume transactional table (Write-Heavy).
- Courses: Metadata for class schedules.
The critical part of this project is demonstrating Indexing Strategies.
When querying for a single student's monthly logs without an index, MySQL scans the entire dataset.
- Rows Scanned: ~100,000
- Query Time: Slow (increases with data volume)
We implemented a composite index on (student_id, scan_timestamp).
CREATE INDEX idx_student_time ON attendance_logs(student_id, scan_timestamp);If you use this project in academic research, publications, educational materials, or derivative works, please cite the project.
This repository includes a CITATION.cff file, so GitHub provides a
"Cite this repository" button in the repository sidebar. You can use it
to obtain citations in BibTeX, APA, and other supported formats.
Suggested citation:
Arain, S. U. R. (2026). FaceRecog-DB-Core-SQL (Version 1.0) [Software]. https://github.com/sanaurrehmanarain/FaceRecog-DB-Core-SQL
Author: Sana Ur Rehman Arain
Profession: Data Scientist
GitHub: https://github.com/sanaurrehmanarain
Contact: sana.arain.work@gmail.com
If you build upon this work, attribution is appreciated and helps others discover the original project.
Note: The MIT License requires that the original copyright notice be retained in copies of the Software.
This project is licensed under the MIT License. See the LICENSE file for details.