Robust face recognition based on low frequency DCT coefficients retransforming optimized by CLAHE
Why this work is in the frame
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Bibliographic record
Abstract
The performance of face recognition is seriously impacted by illumination,expression,posture and occlusion variations,for which low frequency Discrete Cosine Transform(DCT)coefficients retransforming based on Contrast Limiting Adaptive Histogram Equalization(CLAHE)is proposed.Original images are divided into some non-overlapping patches and CLAHE is used to do local contrast stretching so as to reduce noise.Illustration variation of face image is removed by reducing suit numbers of low frequency DCT coefficients.Kernel principle component analysis is used to extract features.Nearest neighbor classifier is used to finish classification and recognition.The effectiveness and reliability of proposed algorithm have been verified by experiments on ORL,extended YaleB and AR face database.Experimental results show that proposed algorithm has higher recognition accuracy than several advanced standardized technologies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it