Deep adaptive convolutional neural network for near infrared and thermal face recognition
Why this work is in the frame
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Bibliographic record
Abstract
Deep Learning algorithms have been widely used for different surveillance tasks in recent years, including people monitoring and counting, abnormal behavior identification, and video segmentation. In most situations, it is assumed that the input images are of high visual quality to provide good performance. When the input data is degraded by variables such as high noise or poor lighting conditions accuracy may degrade. We address the illumination issue in this paper by adapting a face recognition algorithm to near-infrared and thermal images. In this study, we propose a fine-tuning approach to allow deep CNN models to be applied to infrared face recognition (NIR and thermal spectrum). The obtained results with the proposed architecture and infrared images show promising results in deep face recognition with a VAR of 96.68% for the NIR dataset and a VAR of 94.57% for the thermal dataset.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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