StepGAN: Gait Feature Extraction Using Generative Adversarial Networks for Footstep Recognition
Bibliographic record
Abstract
Pressure recordings of footsteps during walking can offer a convenient biometric recognition method for applications in security, forensic analysis, and health monitoring. However, footsteps can exhibit high variability due to a complex interplay of internal and external factors, posing a challenge for recognition systems. To address this issue, this study employed generative adversarial networks with a second discriminator and triplet loss to extract features from high-resolution foot pressure images. By mapping footstep data with different footwear conditions to a shared domain using barefoot pressure, the proposed StepGAN feature extractors significantly improved balanced accuracies from 93.3-95.7% to 96.8-98.0% for verification of 20 individuals with support vector machine classification. This improvement was evident even for users and conditions not included during network training, which highlights the potential of deep-generative models to learn distinctive and generalizable footstep representations. Future studies are recommended to expand these ideas to other factors that contribute to variability. Several potential research directions have been identified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".