Hyperspectral face recognition via feature extraction and CRC‐based classifier
Why this work is in the frame
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Bibliographic record
Abstract
Hyperspectral face recognition provides improved classification rates due to its abundant information in the face cubes of every subject in hyperspectral face databases. However, it is less popular in face recognition due to its difficulty in data acquisition, low signal‐to‐noise ratio, and high dimensionality. The authors compare five existing descriptors that are frequently used in 2D face recognition, and use collaborative representation classifier (CRC) with two voting techniques for hyperspectral face recognition. Experimental results demonstrate that, for PolyU‐HSFD database, Gabor filter bank‐based features are very robust to both Gaussian white noise and shot noise, and it achieves very competitive classification results. For CMU‐HSFD database, when the noise level is low, histogram of oriented gradients (HOG) yields good classification results. In addition, when the noise level is high, raw facial images without feature extraction perform very well in term of correct classification rate. The local binary pattern and HOG descriptor are very sensitive to noise even though they achieve rather good classification rates if the facial images contain no noise. The best recognition result for the PolyU‐HSFD is 96.4% ± 2.3 and for the CMU‐HSFD is 98.0% ± 0.7.
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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 | 0.000 | 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