Design of a Safe Security System Based on Internet of Things Using Face and Fingerprint Detection
Bibliographic record
Abstract
Traditional safe security systems usually use manual keys and a combination of numbers or passwords to open the safe. This system has several disadvantages such as being easy to break into, cumbersome, and the owner easily loses the key, even forgets the password needed to open the safe, which causes the safe to be unable to open. This research develops an Internet of Things (IoT)-based safe security system that uses two security options to open the safe, namely face detection and fingerprint authentication to increase security against unauthorized access with a prototype method. The system uses the ESP32-CAM to capture facial images and send them to the Telegram app for manual verification by the owner, while the fingerprint sensor ensures only registered users can open the safe. Arduino Uno serves as the main microcontroller to manage the integration between components such as ESP32-CAM, fingerprint sensor, relay, solenoid lock, LCD, and buzzer. The test results show that this system is effective in providing security to the safe through notifications, although it still relies on manual verification of faces via Telegram and requires a stable internet connection, and fingerprints that have been registered are successfully implemented. Further development is recommended to automate face recognition and improve the overall performance of the system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".