Quasi-Quantitative Evaluation of Overexposure in the Facial Image for Supporting Portrait Selection
Why this work is in the frame
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Bibliographic record
Abstract
In the selection of portraits for ID and a profile, it is useful to retrieve high quality facial images from a lot of portraits. In addition, overexposure may give a negative impact on us because overexposed facial parts look unnaturally white; therefore the recognition for qualitative meaning of overexposure is important for retrieval of high quality facial images. To get qualitative meaning by a image analysis method that can evaluate images quantitatively, it needs a quasi-quantitative evaluation method that can use an index of quantitative evaluation having qualitative meaning. In this paper, we investigated the index for quasi-quantitative estimation of overexposure in the facial image on the basis of the heuristic assumptions, and defined the index as the overexposure intensity (OI). We developed an algorithm with Support Vector Machine (SVM) for judging the OI. The results of experiment conducted with 11 evaluators for analyzing images suggest that the proposed method can retrieve a facial image for ID and the profile with an F-measure of 96.2%.
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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 | 0.002 | 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 | 0.002 |
| 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