Automated Human Recognition in Surveillance Systems: An Ensemble Learning Approach for Enhanced Face Recognition
Bibliographic record
Abstract
In the realm of surveillance, closed-circuit television (CCTV) cameras serve as a vigilant watch over unfamiliar entities.However, the unpredictability of such entities necessitates continuous human monitoring, an endeavor prone to error and demanding of significant resources.The automation of this process through face recognition could alleviate these burdens, provided the system delivers high precision and rapid judgment capabilities.This study presents a novel solution to these challenges: an automated human recognition and verification surveillance system, founded on a max-voting ensemble method.This innovative approach amalgamates five influential feature extraction models: VGGFace, FaceNet, FaceNet-512, Dlib, and Arcface, with a support vector machine deployed for classification.The proposed system was subjected to rigorous testing on the AT&T, faces94, Grimace, Georgia Tech, and FaceScrub datasets, demonstrating an impressive accuracy of 100% on the AT&T, faces94, and Grimace datasets, and 99.3% and 98% on the Georgia Tech and FaceScrub datasets, respectively.The system's performance was further enhanced through a re-verification technique, which facilitated swift and precise prediction of unknown entities in real time.This study thus contributes a significant advancement to the field of automated surveillance, offering a potent tool for efficient, accurate human recognition.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".