Experimental investigation of facial expressions associated with visual discomfort: Feasibility study toward an objective measurement of visual discomfort based on facial expression
Bibliographic record
Abstract
This paper aims to investigate facial expressions associated with visual discomfort induced by excessive screen disparities of stereoscopic three-dimensional (S3D) contents. For this purpose, we constructed a novel facial expression database regarding the visual discomfort. While viewing the realistic stereoscopic stimuli with screen disparities varying from 0° to 4.66°, each viewer's face was captured. The database consisted of face videos and associated comfort scores obtained by self-reporting, which might be only a publicly available database regarding the facial expressions associated with visual discomfort. Using the database, for the quantitative investigation, the facial expressions associated with visual discomfort were compared with basic emotional expressions that were well defined and universal. As a result, we observed that the emotional expression of “stressed” (i.e., anger or disgust) was highly correlated with the perceived visual discomfort (Pearson correlation coefficient: 0.91). Furthermore, the feasibility of the discomfort measurement using facial expressions obtained while viewing S3D contents was verified. Experimental results showed that the discomfort measurement using facial expression recognition could achieve a feasible performance (classification accuracy of 81.42%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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 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".