Face Recognition Assistant for People with Visual Impairments
Bibliographic record
Abstract
Although there are many face recognition systems to help individuals with visual impairments (VIPs) recognize other people, almost all require a database with the pictures and names of the people who should be tracked. These solutions would not be able to help VIPs recognize people they might not know well. In this work, we investigate the requirements and challenges that must be addressed in the design of a face recognition system for helping VIPs recognize people with whom they have weak-ties. We first conducted a formative study with eight visually impaired people. Using insights learned from the formative study, we developed a research prototype that runs on a mobile phone worn around the user's neck. The developed prototype is a wearable face recognition system that opportunistically captures and stores undistorted face images and contextual information about the user's interaction with each person to a database, without the user intervention, as she interacts with new people. We then used this prototype application as a technology probe---asking VIP participants to use the device in a realistic scenario in which they meet and re-encounter several new people. We analyze and report feedback collected from VIPs about the design and use of such a service.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".