Investigating the Role of Key Facial Features for Face Detection
Bibliographic record
Abstract
An important body of literature is dedicated to understanding how humans compute the complex information required for face recognition. Specifically, many have stated that the eyes and mouth regions play a fundamental role in this process, supported by evidence from individual differences and prosopagnosic patients (Caldara et al., 2005; Royer et al., 2018; Tardif et al., 2019). While psychophysical studies have established the importance of these regions in identification, few studies have explored the preceding step: face detection. That said, a study by Xu and Biederman (2014) suggests a link between these two processes, showing that a prosopagnosic patient experienced notable difficulties in a face detection task, suggesting that the same facial information might be used for both processes. The present study therefore aims to identify the key regions involved in face detection, a crucial step before delving deeper into this aspect in patients with acquired prosopagnosia. In this study, twenty participants completed 3,000 trials divided in two face detection tasks (1 - Does the presented stimulus contain a face? 2 - Which of the two stimuli contains a face?), with the non face stimuli being 100% wavelet decomposed faces (Koenig-Robert & VanRullen, 2013). In both tasks, stimuli were overlaid with Bubbles across five spatial frequency (SF) bands (Gosselin & Schyns, 2001). The resulting classification images reveal that face detection relies on all facial features (eyes, nose, and mouth, p = .001) across all spatial frequency bands, with the presence of the eyes being associated with higher Z-scores. These results suggest that face detection and face recognition processes are closely linked, as both rely on the same facial regions to accomplish the task. This opens new perspectives for understanding and diagnosing deficits associated with prosopagnosia, but also for better understanding the underlying processes of face recognition.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 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".