Filter‐based face recognition under varying illumination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, the authors develop a new algorithm for face recognition with varying lighting conditions. Their method first performs low‐pass and high‐pass filtering to the face image, and then takes the ratio between the two filtered images. The authors take the arctangent to the ratio and use these features to classify an unknown face image. In addition, their method works for any combination of low‐pass and high‐pass filters. The authors studied two sets of the low‐pass and high‐pass filters in their experiments and their results are better than gradient faces, Weber faces, and self‐quotient images (SQIs) in the noisy environment, no matter denoising or no denoising is performed to the noisy face images for the CMU‐PIE and Extended Yale‐B databases. Nevertheless, the SQI is best for the CAS‐PEAL face database in authors’ experiments. The SQI takes a convolution operation with a low‐pass filter. The implementation of SQI may have chosen a more suitable low‐pass filter for the CAS‐PEAL face database, but not for the YALE and CMU‐PIE face databases. This may be the main reason why SQI outperformed their proposed method in this study for the CAS‐PEAL face database. Nevertheless, the SQI is many times slower than their proposed method.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it