Gait Representation: from Vision-Based to Floor Sensor-Based Gait Recognition
Bibliographic record
Abstract
Footstep recognition is a form of biometric authentication that verifies or identifies individuals by their unique underfoot pressure patterns. The selection of an appropriate gait representation (i.e., feature extraction technique) therefore plays an important role in its performance. This investigation explores twenty state-of-the-art 2D gait representations for foot-step recognition, including several appearance representations established for vision-based gait recognition that are novel to the field. In this study, the focus is on access control scenarios (such as high-security facilities, airport checkpoints, or sacred places) where only a small amount of footsteps are available for model training and users may be expected to be barefoot. While peak pressure image features showed the best performance for identity verification across many operating points (an average balanced accuracy of 93% across 86 user models), appearance representations provided meaningful and non-redundant information and should therefore be considered in combination with traditional pressure and time-based representations for developing footstep authentication systems. This paper informs the design and selection of gait representations for floor sensor-based gait recognition, and discusses some possible solutions for developing verification systems with limited training samples.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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; both teacher heads agree on what is shown here.
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".