Multimodal biometric identification system for mobile robots combining human metrology to face recognition and speaker identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recognizing a person from a distance is important to establish meaningful social interaction and to provide additional cues regarding the situations experienced by a robot. To do so, face recognition and speaker identification are biometrics commonly used, with identification performance that are influenced by the distance between the person and the robot. This paper presents a system that combines these biometrics with human metrology (HM) to increase identification performance and range. HM measures are derived from 2D silhouettes extracted online using a dynamic background subtraction approach, processing in parallel 45 front features and 24 side features in 400 ms compared to 38 front and 22 side features extracted in sequence in 30 sec by using the approach presented by Lin and Wang [1]. By having each modality identify a set of up to five possible candidates, results suggest that combining modalities provide better performance compared to what each individual modality provides, from a wider range of distances.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it