Bibliographic record
Abstract
Abstract: The Face Recognition Attendance System represents a modern biometric-based solution specifically designed to automate the process of tracking attendance using facial recognition technology. This innovative system addresses and resolves several limitations and inefficiencies that are commonly associated with traditional attendance methods, including manual roll calls, sign-in sheets, and the use of RFID cards. By minimizing human intervention, it significantly reduces the occurrence of human errors and prevents fraudulent practices such as proxy attendance. The system integrates real-time image acquisition, facial detection algorithms, and advanced deep learning models to ensure a high level of accuracy and reliability in personal identification. Furthermore, the interface of the application is intentionally designed to be user-friendly, making it easy for administrators and users to operate and manage the system effectively. The inclusion of a structured and optimized database architecture ensures streamlined data storage, retrieval, and management. Additionally, the contactless mode of operation aligns with current health and safety guidelines by minimizing physical interaction, thereby supporting hygienic practices in public and private institutions. Through comprehensive testing across a variety of environmental and user conditions, the system has proven to maintain consistent performance and robustness. Future improvements under consideration involve the incorporation of features such as real-time face mask detection, integration with cloud-based storage systems for scalable data access, and the application of more sophisticated artificial intelligence capabilities. This system is particularly suitable for deployment in educational institutions, corporate offices, and industrial sectors where secure, efficient, and tamper-proof attendance recording mechanisms are essential.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.024 |
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".