ARTriViT: Automatic Face Recognition System Using ViT-Based Siamese Neural Networks with a Triplet Loss
Why this work is in the frame
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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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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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