Article Using Virtual Heads for Person Identification: An Empirical Study Comparing Photographs to Photogrammetrically-Generated Models
Bibliographic record
Abstract
Abstract: The purpose of this study was to examine the effectiveness of virtual heads (i.e., three-dimensional models of human heads and faces). Our goal was to test these virtual head models as functional substitutes for photographs of humans as well as for live humans during eyewitness lineups and other processes relating to person identification. We tested the effectiveness of virtual heads by taking photographs of people and then using 3DMeNow software by bioVirtual to build three-dimensional models that resembled the photographs. We tested to see how easily experimental subjects would recognize images of the three-dimensional models (compared to photographs) after being trained on short video clips of people. The goal of this study was to examine subjects ’ recognition of virtual faces and to compare this performance to recognition of real faces [1]. In the following sections, we discuss relevant previous research, present the methods and results of the current study, and finally point to directions for future work. This paper was presented at the 88th annual educational conference of the International Association for Identification held in Ottawa, Canada,
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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 | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".