Applying Contrast-limited Adaptive Histogram Equalization and integral projection for facial feature enhancement and detection
Bibliographic record
Abstract
In order to achieve real-time subject-independent automatic facial feature enhancement and detection, a novel method is presented in this paper combing Contrast-limited Adaptive Histogram Equalization (CLAHE) and multi-step integral projection. First, after real-time detecting face images, a sigma filter is used to remove the noise in images. Sigma filtering is chosen in this research because of its validity in noise removal. It has the advantages of providing a good noise removal result, not blurring the image and fast performance. Second, since it is important to extract facial features as accurately and clearly as possible, CLAHE is then applied on images for enhancing the facial features. This step is done after the sigma filter in order not to amplify the noise in images. Third, after enhancing these features, multi-step integral projection is proposed to detect the useful facial features regions automatically. Finally, the detected facial feature region is then extracted by Gabor transformation and the final facial expression recognition is classified by SVMs. We test our system on the JAFFE database and achieve a high recognition rate of 95.318% on trained data.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".