A LIME-Explained VGG16 Model for Disguise and Makeup Face Recognition in Forensics
Bibliographic record
Abstract
This paper exploits the rise of artificial intelligence (AI) and deep learning (DL) to improve the use of digital forensic evidence analysis, specifically criminal identification from facial images despite disguise and makeup. Our approach leverages the VGG16 architecture for face recognition and identification, coupled with the LIME framework (Local Interpretable Model-Agnostic Explanations) to explain model recognition. This combination enables interpretation and verification of results with enhanced trust and confidence in forensic analysis. We follow a "watch and iterate" procedure, utilizing the insights generated from LIME to curate the training dataset, improving the model’s performance iteratively. The efficacy of this procedure is reflected in the remarkable outcomes: our model has an accuracy of 98.10%, precision of 98.16%, recall of 98.10%, F1-score of 98.11%, AUC of 100%. This development in forensic technology has great potential to enhance the precision and speed of criminal identification, thus leading to safer and fairer societies.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".