Illumination invariant human face recognition: frequency or resonance?
Bibliographic record
Abstract
In this paper we suggest the use of resonance based decomposition of images for illumination invariant face recognition. Although illumination is mostly considered as the low-frequency part of images, these low-frequency contents may possess low- and/or high-resonance nature. We first assume that an input image can be considered as a combination of illumination and reflectance. The images are then decomposed into low- and high-resonance components simultaneously. Because the energy distribution of subbands of resonance based decomposition are different for an image with good illumination effects and an image with high illumination variations, the energy of subbands of the two components can be thresholded to deactivate the subbands with unwanted energy distribution created by illumination effects. For dimensionality reduction and classification the principal component analysis and extreme learning machine have been used, respectively. Experiments and comparisons illustrate the effectiveness of the proposed resonance based method in illumination invariant face recognition.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".