Scale-space and wavelet decomposition based scheme for face recognition using nearest linear combination.
Bibliographic record
Abstract
Face recognition has attracted much attention from Artificial Intelligence researchers due to its wide acceptability in many applications. Many techniques have been suggested to develop a practical face recognition system that has the ability to handle different challenges. Illumination variation is one of the major issues that significantly affects the performances of face recognition systems. Among many illumination robust approaches, scale-space decomposition based methods play an important role in reducing the lighting effects in facial images. This research presents a face recognition approach for utilizing both the scale-space decomposition and wavelet decomposition methods. In most cases, the existing scale-space decomposition methods perform recognition, based on only the illumination-invariant small-scale features. The proposed approach uses both large-scale and small-scale features through scale-space decomposition and wavelet decomposition. Together with the Nearest Linear Combination (NLC) approach, the proposed system is validated on different databases. The experimental results have shown that the system outperforms many recognition methods in the same category. --Leaf ii.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".