Enhancing thermal facial recognition leveraging large datasets and hybrid algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Face recognition based on thermal image is a crucial aspect of identity verification that has been developed to counter low or no illumination. This paper proposes a novel hybrid algorithm for thermal face recognition to cope with the low resolution and texture blurring of thermal images. The algorithm contains a multi-scale feature fusion module, an attention module, and a joint loss function, which enhances the feature extraction capability, improves the classification accuracy, and has few network parameters. In addition to the innovative approach, a collaborative thermal facial dataset, named CSU-Laval, has been established by combining the 134 ULFMT dataset from Laval University, Canada, with 210 subjects acquired from Central South University, China. This dataset has 344 subjects and contains a rich set of face variables, including expression, angle, glasses-wearing, and time-lapse.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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