Effects of image background on spatial frequency thresholds for face recognition
Bibliographic record
Abstract
A growing number of studies have investigated the question of which spatial frequencies, if any, are optimally useful for face recognition. To our knowledge, all of these studies have used face images with monochromatic backgrounds, usually medium-gray. A potential limitation of this methodology is that it does not accurately reflect the real-world situation to which results are to be generalized. That is, in the real world the visual system must recognize faces against a variety of backgrounds, and the spatial frequencies needed for face recognition may be different in these circumstances than when the background is homogenous. In this study, we investigated the differences in spatial frequency thresholds for face matching across three different types of backgrounds: 1) Monochromatic gray, 2) fractal noise, and 3) natural scenes. Observers were asked to find their matching threshold, using the method of adjustment, in a 4AFC match-to-sample task. That is, four faces were presented at the bottom of the screen, and a high-passed or low-passed face was presented in the middle of the screen. Observers were asked to adjust the cut-off of the spatial frequency filter to the point where they could just match the center face to one of the four comparison faces. Our results show small but consistent differences in threshold according to the type of background surrounding the face. Images with a fractal noise background elicited higher low-pass thresholds and lower high-pass thresholds than did the other two background types. There was no difference between monochromatic gray backgrounds and natural backgrounds. These data support the generalizability of results from studies using monochromatic gray backgrounds to real-world vision. However, the data also suggest that non-structured backgrounds can produce additional difficulty in recognizing spatially filtered face images. Additional data using the Method of Constant Stimuli are also being gathered.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".