InnovFaceNet: Deep Face Recognition for Industrial Environments
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
In recent times the usage of intelligent systems have paved way formany applications to be robust and self-reliant. One such popularand vast growing technology is face recognition. Facial Recognitiontechnology is used in security, surveillance, criminal justice systemsand many other multimedia platforms. This work proposes a realtime facial recognition technology which can be used in any industrialsetup eliminating manual supervision, ensuring authorized accessto the personnel in the plant. Due to the recent development ofCOVID-19 pandemic around the world, wearing masks has becomea necessity. Our proposed facial recognition technology identifies aperson’s face with mask or no mask in real time with a speed of20 FPS on a CPU and an F1-score of 95.07%. This makes ouralgorithm fast, secure, robust and deployable on a simple personalcomputer or any edge device at any industrial plant or organization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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