Adaptive multi-stream score fusion for illumination invariant face recognition
Bibliographic record
Abstract
Quality variations of samples significantly affect the performance of biometric recognition systems. In case of face recognition systems, illumination degradation is the most common contributor of enormous intra-class variation. Wavelet transforms are very popular techniques for face or object recognition from images due to their illumination insensitiveness. However, low and high frequency subbands of wavelet transforms do not possess equal insensitiveness to different degree of illumination change. In this paper, we investigated the illumination insensitiveness of the subbands of Dual-Tree Complex Wavelet Transform (DTCWT) at different scales. Based on the investigations, a novel face recognition system has been proposed using weighted fusion of low and high frequency subbands that can adapt extensive illumination variations and produces high recognition rate even with a single sample. A novel fuzzy weighting scheme has been proposed to determine the adaptive weights during uncertain illuminations conditions. In addition, an adaptive normalization approach has been applied for illumination quality enhancement of the poor lit samples while retaining the quality of good samples. The performance of the proposed adaptive method has been evaluated on Extended Yale B and AR face databases. Experimental results exhibit significant performance improvement of the proposed adaptive face recognition approach over benchmark methods under extensive illumination change.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".