Face Recognition System for Criminal Identification in CCTV Footage Using Keras and OpenCV
Bibliographic record
Abstract
This paper presents an advanced face recognition system for identifying potential criminals using Closed Circuit Television (CCTV) footage.The model is trained using a diverse dataset comprising facial images with variations in age, ethnicity, gender, lighting conditions, and facial expressions (such as smiling, frowning, and wearing glasses).The system leverages deep learning techniques, specifically a Convolutional Neural Network (CNN) with a pre-trained VGG16 architecture integrated with the Keras library for extracting complex facial features.OpenCV is utilized for video preprocessing, frame extraction, and real-time deployment.The model utilizes transfer learning, optimized with the Adam algorithm and a cross-entropy loss function, to enhance its generalization across diverse facial features.The VGG16 model demonstrates strong performance, achieving an accuracy of 97.6%, recall of 96.9%, precision of 97.5%, and an F1-score of 96.9%.This system is designed for real-time surveillance applications, ensuring fast and accurate criminal identification with minimal human intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".