Facial biometrics based on 2D vector geometry
Bibliographic record
Abstract
The main challenge of facial biometrics is its robustness and ability to adapt to changes in position orientation, facial expression, and illumination effects. This research addresses the predominant deficiencies in this regard and systematically investigates a facial authentication system in the Euclidean domain. In the proposed method, Euclidean geometry in 2D vector space is being constructed for features extraction and the authentication method. In particular, each assigned point of the candidates’ biometric features is considered to be a 2D geometrical coordinate in the Euclidean vector space. Algebraic shapes of the extracted candidate features are also computed and compared. The proposed authentication method is being tested on images from the public “Put Face Database”. The performance of the proposed method is evaluated based on Correct Recognition (CRR), False Acceptance (FAR), and False Rejection (FRR) rates. The theoretical foundation of the proposed method along with the experimental results are also presented in this paper. The experimental results demonstrate the effectiveness of the proposed method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".