Classifying Affective States Using Thermal Infrared Imaging of the Human Face
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, time, frequency, and time-frequency features derived from thermal infrared data are used to discriminate between self-reported affective states of an individual in response to visual stimuli drawn from the International Affective Pictures System. A total of six binary classification tasks were examined to distinguish baseline and affect states. Affect states were determined from subject-reported levels of arousal and valence. Mean adjusted accuracies of 70% to 80% were achieved for the baseline classifications tasks. Classification accuracies between high and low ratings of arousal and valence were between 50% and 60%, respectively. Our analysis showed that facial thermal infrared imaging data of baseline and other affective states may be separable. The results of this study suggest that classification of facial thermal infrared imaging data coupled with affect models can be used to provide information about an individual's affective state for potential use as a passive communication pathway.
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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 | 0.000 |
| 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