Evaluating the Feasibility of Emotion Expressions in Avatars Created From Real Person Photos: Pilot Web-Based Survey of Virtual Reality Software
Bibliographic record
Abstract
BACKGROUND: The availability and potential of virtual reality (VR) has led to an increase of its application. VR is suggested to be helpful in training elements of social competence but with an emphasis on interventions being tailored. Recognizing facial expressions is an important social skill and thus a target for training. Using VR in training these skills could have advantages over desktop alternatives. Children with autism, for instance, appear to prefer avatars over real images when assessing facial expressions. Available software provides the opportunity to transform profile pictures into avatars, thereby giving the possibility of tailoring according to an individual's own environment. However, the emotions provided by such software should be validated before application. OBJECTIVE: Our aim was to investigate whether available software is a quick, easy, and viable way of providing emotion expressions in avatars transformed from real images. METHODS: A total of 401 participants from a general population completed a survey on the web containing 27 different images of avatars transformed, using a software, from real images. We calculated the reliability of each image and their level of difficulty using a structural equation modeling approach. We used Bayesian confirmatory factor analysis testing under a multidimensional first-order correlated factor structure where faces showing the same emotions represented a latent variable. RESULTS: Few emotions were correctly perceived and rated as higher than other emotions. The factor loadings indicating the discrimination of the image were around 0.7, which means 49% shared variance with the latent factor that the face is linked with. The standardized thresholds indicating the difficulty level of the images are mostly around average, and the highest correlation is between faces showing happiness and anger. CONCLUSIONS: Only using a software to transform profile pictures to avatars is not sufficient to provide valid emotion expressions. Adjustments are needed to increase faces' discrimination (eg, increasing reliabilities). The faces showed average levels of difficulty, meaning that they are neither very difficult nor very easy to perceive, which fits a general population. Adjustments should be made for specific populations and when applying this technology in clinical practice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".