Face Biometric Fairness Evaluation on Real vs Synthetic Cross-Spectral Images
Bibliographic record
Abstract
In this paper, we compare the performance and fairness metrics on visual and thermal images of faces, including the synthetic images of human subjects with face masks. The comparative experiment is performed on two datasets: the SpeakingFace and Thermal-Mask dataset. We assess fairness on real images and show how the same process can be applied to synthetic images. The chosen fairness metrics include demographic parity difference and equalized odds difference. While the demographic parity difference is assessed as 1.24 for random guessing in the process of face identification, it reaches 5.0 when both the precision and recall rate approach 99.99%. These results confirm that inherently biased datasets significantly impact the fairness of any biometric system. For biometric-enabled systems, fairness is related to the adequacy of the data to represent different groups of human subjects. In this paper, we focus on three demographic groups: age, gender, and ethnicity. A primary cause of biases with respect to these groups is the class imbalance introduced through the data collection process. To address the imbalanced datasets, the classes with fewer samples can be augmented with synthetic images to generate a more balanced dataset, resulting in less bias when training a machine learning system. The study shows that fairness is correlated to the performance of the system rather than to the genesis of the images (real or synthetic). The experiment on a simple 3-Block CNN with a precision and recall rate of 99.99% using the demographic parity difference as an estimate of fairness showed that among gender, ethnicity, and age, the latter is an attribute that is the most sensitive while age is the least one.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".