Spontaneous Pain Expression Recognition in Video Sequences
Why this work is in the frame
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Bibliographic record
Abstract
Automatic recognition of Pain expression has potential medical significance. In this paper we present results of the application of an automatic facial expression recognition system on sequences of spontaneous Pain expression. Twenty participants were videotaped while undergoing thermal heat stimulation at nonpainful and painful intensities. Pain was induced experimentally by use of a Peltierbased, computerized thermal stimulator with a 3 × 3 cm 2 contact probe. Our aim is to automatically recognize the videos where Pain was induced. We chose a machine learning approach, previously used successfully to categorize the six basic facial expressions in posed datasets [1, 2] based on the Transferable Belief Model. For this paper, we extended this model to the recognition of sequences of spontaneous Pain expression. The originality of the proposed method is the use of the dynamic information for the recognition of spontaneous Pain expression and the combination of different sensors: facial features behavior, transient features and the context of the expression study. Experimental results show good classification rates for spontaneous Pain sequences especially when we use the contextual information. Moreover the system behaviour compares favourably to the human observer in the other case, which opens promising perspectives for the future development of the proposed system.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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