ARTriViT: Automatic Face Recognition System Using ViT-Based Siamese Neural Networks with a Triplet Loss
Bibliographic record
Abstract
Computer-based face recognition and other biometric techniques are now mature and trustworthy technology that plays a crucial role in many access control scenarios. Face recognition undergoes a variety of difficulties, including those related to angle, lighting, position, facial expression, noise, resolution, occlusion, and the scarcity of samples from each class. In this study, we proposed a triplet loss-based Siamese network with a vision transformer as a feature extractor instead of traditional convolution. Our Siamese analyzes a pair of face images as input, extracts the characteristics from these pairs, and uses similarity indexes to evaluate them for face recognition using the Celeb-DF (version 2) dataset. As a result, the suggested model performs well compared to the state-of-the-art (SOTA) on the Celeb-DF version 2 dataset. The trained model and code will be available at: https://github.com/MuhammadSaeedMBZUAINiTBased-Siamese.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".