Development of a Dual Biometric Authentication System Based on IoT with Facial Recognition and Fingerprint for Safe Security
Bibliographic record
Abstract
Protecting valuable assets requires a security system that adapts to modern challenges, where physical keys or numerical codes on conventional safes are vulnerable to loss, duplication, and breaking. This research develops an Internet of Things (IoT)-based dual biometric authentication system with face recognition using ESP32-CAM and AS608 fingerprint verification performed sequentially, granting access only if both authentication stages are successful. The NodeMCU ESP32 is used as the main controller, integrating the authentication process with the solenoid lock and buzzer, and utilizing Firebase Realtime Database for real-time monitoring of status and access history through an Android application. Unlike previous research, which only used face recognition with manual verification via Telegram, this system is fully automated with multi-layer authentication, making it more secure and efficient. The prototyping method is used to design, program, and test the system, with testing results showing 100% success in opening the safe only for registered users, making this system more reliable than single authentication.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".