Class Attendance System Using Facial Recognition
Bibliographic record
Abstract
This study aimed to develop an automated attendance system using facial recognition technology to address the challenges of traditional methods like roll calls and swipe cards.The system would capture and verify student identities through facial recognition, making it more hygienic and user-friendly.The project aligned with Sustainable Development Goals (SDGs) such as Quality Education, Industry Innovation, and Responsible Consumption and Production.It enhances Quality Education by promoting accurate and efficient attendance tracking, reducing administrative workload, and allowing educators to focus more on teaching.By integrating advanced technology into daily educational practices, it aligns with Industry Innovation, showcasing practical applications of biometric systems.Additionally, it contributes to responsible resource use by minimizing the need for paper-based records, thus aligning with sustainable practices.The system involved creating a database to store student facial features, extracting facial features for identity verification, and generating attendance records.Performance was evaluated through testing, focusing on factors like threshold values (where a value of 0.5 provided optimal performance), lighting conditions, and camera quality.The results showed high accuracy in student identification and attendance recording, and the system allowed for data exportation to CSV files and a user-friendly interface.However, the system's performance was affected by environmental conditions, indicating areas for further optimization.The implementation of this facial recognition attendance system has significant implications for educational institutions, enhancing efficiency, security, and promoting an inclusive administrative process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".