Room Security System Using Machine Learning with Face Recognition Verification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine Learning (ML), an intelligent system known for its capacity to automate procedures by discerning patterns pertinent to specific tasks such as detection, prediction, and pattern recognition, is increasingly being used to advance biometric technologies.Among these, facial recognition, a subset of computer vision-based biometrics, is emerging as a robust security measure.The present study is centered on the design of a room security system that leverages facial recognition, rooted in a Convolutional Neural Network (CNN) architecture.The CNN model was constructed within the Tensorflow framework, employing the Keras library and Scikit-learn, all embedded within a Raspberry Pi system.The model was trained on 15 registered face classes, with an additional three unregistered classes used for biometric security testing.Performance was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR), metrics that assess the system's ability to accurately verify authorized and unauthorized users.Findings demonstrated that the CNN model achieved a 97% accuracy rate in facial identification.Furthermore, biometric security testing of the CNN model using room security devices yielded optimal results at a threshold of 90%, with FAR=26.67%,FRR=9.33%, and an Equal Error Rate (EER) of 21.33%.It was observed that factors such as lighting, data variation, resolution, and positional changes during data sampling could impact the system's performance in realtime operations.It is therefore recommended that data collection and facial scanning be consistently conducted under identical environmental conditions to enhance the accuracy of the system.This study signifies a substantial stride in the development of advanced room security systems, thus contributing to the broader realm of secure access control systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it