Soft biometric: Give me your favorite images and i will tell your gender
Bibliographic record
Abstract
Gender estimation for security and forensic purposes is not a trivial task. Recently, researchers provided methods for predicting gender based on face-images, fingerprint ridge density, body shape, voice and gait. No research to date have been concerned with using one's image aesthetic preferences for predicting gender. Cognitively and psychologically, males and females have different visual aesthetic preferences. This paper is a proof of concept that it is possible to use image's perceptual aesthetic features to identify the gender of a person. This article identifies a bag of image aesthetic features and selects a number of most differentiating features using filter and wrapping selection methods. To improve the classification accuracy, weighted combination of decisions obtained by the conventional binary classifiers is used. The final decision is made based on the fusion of probabilities generated by the mixture of classifiers. The prediction model is trained and tested on a database consisting of 24000 images from 120 Flickr users. Experiment shows that a proper weight assignments allows to obtain 77% accuracy in gender prediction based on aesthetics alone.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.018 |
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".