FaceUI: Leveraging Front-Facing Camera Input to Access Mid-Air Spatial Interfaces on Smartphones
Bibliographic record
Abstract
We present FaceUI, a novel strategy to access mid-air face-centered spatial interfaces with off-the-shelf smartphones. FaceUI uses the smartphone's front-facing camera to track the phone's mid-air position relative to the user's face. This self-contained tracking mechanism opens up new opportunities to enable mid-air interactions on off-the-shelf smartphones. We demonstrate one possibility that leverages the empty mid-air space in front of the user to accommodate virtual windows which the user can browse by moving the phone in the space in front of their face. We inform our implementation of FaceUI by first studying essential design factors, such as the comfortable face-to-phone distance range and appropriate viewing angles for browsing mid-air windows and visually accessing their content. After that, we compare users' performance with FaceUI to their performance when using a touch-based interface in an analytic task that requires browsing multiple windows. We find that FaceUI offers better performance than the traditional touch-based interface. We conclude with recommendations for the design and use of face-centered mid-air interfaces on smartphones.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".