Face Recognition based on Logarithmic Fusion of SVD and KT
Bibliographic record
Abstract
The identification of a person based on biometric is accurate and robust compared to traditional methods of identifying a person using PIN, ID cards etc., In this paper Face Recognition based on Logarithmic Fusion of SVD and KT (FRLSK) is proposed. The Singular Value Decomposition (SVD) is applied on face images to derive Co-efficients. The Co-efficient Matrix of SVD are resized to 64x64 to form features. The test image SVD features are compared with SVD feature of database images using Euclidian distance, Equal Error Rate (EER) and Total Success Rate are computed (TSR). The Kekre Transform (KT) is applied on Resized (64x64) face images to form features. The test image KT Features are compared with KT features of Database images using Euclidian distance to compute EER and TSR. The EER and TSR values obtained by SVD techniques are fused with the value of EER and TSR obtained from KT using logarithmic transforms to get better value of EER and TSR. It is observed that the value of EER and TSR are better in the case of proposed algorithm compared to existing algorithm.
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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 | 0.001 | 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".