Comparing Face-Tracking Action Units with EMG During Speech
Bibliographic record
Abstract
Video face-tracking software such as OpenFace 2.2 can make inferences about facial muscle activation (Baltrušaitis et al. 2018). However, it is unclear what the accuracy of these inferences is based on the facial action units (FAUs) calculated by OpenFace 2.2 compared to corresponding muscle activity during speech A previous study investigated muscle activation when a smile and speech co-occur, focusing on the Zygomaticus Major (ZM) and Orbicularis Oris (OO) muscles (Liu et al. 2021), but only presented data for a single speaker and did not compare FAU and EMG results. The present study compares OpenFace 2.2 action units with surface electromyography (EMG) data during speech in order to assess the validity of these inferences about facial muscle activation. We compare ZM activity with the lip corner puller FAU intensity results and OO activity with the lip-tightener FAU intensity results from a dataset collected for the previously mentioned Liu et al. (2021) study. Data includes four speakers producing read speech in smile conditions. We will report the results of a study extracting and analyzing 4-second segments from EMG and FAU data, recorded before and after each utterance, using Python for efficient data processing and graphical comparison. Our analysis to date indicates a relative correspondence between EMG and FAU data indicating the possible reliability of video face-tracking software in research applications. Details will be reported.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".