Soft biometric: Give me your favorite images and i will tell your gender
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it