Adaptive Face Recognition Based on Image Quality
Bibliographic record
Abstract
Quality of facial images has a great impact on the accuracy of the automated face recognition system. The intraclass variations introduced by varied facial quality due to variation in illumination conditions may degrade the performance of a face recognition system significantly. In this paper, we proposed an adaptive discrete wavelet transform (DWT) based face recognition approach which will normalize the illumination distortion using regional contrast limited adaptive histogram equalization (CLAHE) and discrete cosine transform (DCT) normalization based on the illumination quality. The DWT based approach is used to extract the low and high frequency facial features at different scales. In the proposed method, a weighted fusion of the low and high frequency subbands is computed to improve the identification accuracy under varying lighting conditions. The selection of fusion parameters is made using fuzzy membership functions. The performance of the proposed method was validated on the Extended Yale Database B. Experimental results depict that the proposed method outperforms some well known face recognition approaches.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".