Adaptive Region-Based Image Enhancement Method for Robust Face Recognition Under Variable Illumination Conditions
Bibliographic record
Abstract
Variable illumination conditions, especially the side lighting effects in face images, form a main obstacle in face recognition systems. To deal with this problem, this paper presents a novel adaptive region-based image preprocessing scheme that enhances face images and facilitates the illumination invariant face recognition task. The proposed method first segments an image into different regions according to its different local illumination conditions, then both the contrast and the edges are enhanced regionally so as to alleviate the side lighting effect. Different from existing contrast enhancement methods, we apply the proposed adaptive region-based histogram equalization on the low-frequency coefficients to minimize illumination variations under different lighting conditions. Besides contrast enhancement, by observing that under poor illuminations the high-frequency features become more important in recognition, we propose enlarging the high-frequency coefficients to make face images more distinguishable. This procedure is called edge enhancement (EdgeE). The EdgeE is also region-based. Compared with existing image preprocessing methods, our method is shown to be more suitable for dealing with uneven illuminations in face images. Experimental results on the representative databases, the Yale B+Extended Yale B database and the Carnegie Mellon University-Pose, Illumination, and Expression database, show that the proposed method significantly improves the performance of face images with illumination variations. The proposed method does not require any modeling and model fitting steps and can be implemented easily. Moreover, it can be applied directly to any single image without using any lighting assumption, and any prior information on 3-D face geometry.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".