An Efficient Facial Expression Recognition System in Infrared Images
Bibliographic record
Abstract
Most of the reported algorithms for facial expression recognition (FER) are based on visible expression databases. However, visible images are affected by illumination variations which can cause significant disparities in image appearance and texture. In this paper preliminary results of a new FER algorithm, using Gauss-Laguerre (GL) filter of circular harmonic wavelets to extract features for infrared images, are presented. By using GL filters with properly tuned-parameters, it is possible to generate a set of redundant wavelets that enable an accurate extraction of complex texture features from an infrared image. In addition, we utilize GL filters that are highly suitable for FER in visible images. The combination of infrared and visible FER using common feature extraction approach saves time and reduces the complexity in a multiple-sensors scenario. K-nearest neighbor is used for classification on OTCBVS and USTC-NVIE databases. The results show effective performance for using GL filters in FER for infrared images.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".