Are two views better than one? Investigating three-quarter view facial composites
Bibliographic record
Abstract
Purpose – The introduction of a three-quarter-view database in the PRO-fit facial-composite system has enabled an investigation into the effects of image view in face construction. The purpose of this paper is to examine the impact of constructing full-face and three-quarter-view composites under different encoding conditions. It also examines the potential value of three-quarter-view composites that can be generated automatically from a front-view composite. The authors also investigate whether there is an identification benefit for presenting full-face and three-quarter composites together. Design/methodology/approach – Three experiments examine the impact of encoding conditions on composite construction and presentation of composites at the evaluation stage. Findings – The work revealed that while standard full-face composites perform well when all views of the face have been encoded, care should be taken when a person has only seen one view. When a witness has seen a side view of a suspect, a three-quarter-view composite should be constructed. Also, it would be beneficial for a witness to construct two composites of a suspect, one in full-face view and one in a three-quarter-view, particularly when the witness has only encoded one view. No benefit emerged for use of three-quarter-view composites generated automatically. Research limitations/implications – This is the first study to examine viewpoint in facial composite construction. While a great deal of research has examined viewpoint dependency in face recognition tasks, composite construction is a reconstruction task involving both recall and recognition. The results indicate that there is a viewpoint effect that is similar to that described in the recognition literature. However, more research is needed in this area. Practical implications – The practical implications of this research are that it is extremely important for facial composite operators in the field (police operators) to know who will make a good likeness of the target. Research such as this which examines real-life issues is incredibly important. This research shows that if a witness has seen all views of a perpetrator’s face then standard composite construction using a full-face view will work well. However, if they have only seen a single view then it will not. Social implications – There are obvious wider societal implications for any research which deals with eyewitness memory and the potential identification of perpetrators. Originality/value – No research to date has formally examined the impact of viewpoint in facial-composite construction.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".