Gait Recognition Using EigenfeetNet
Bibliographic record
Abstract
Due to a growing emphasis on personal privacy and non-intrusive methods of person authentication, researchers have sought to evaluate new and emerging biometric technologies. One promising approach is gait recognition using pressure-sensitive flooring (or footstep recognition) which strives to verify a person's identity using the patterns of pressures exerted on the floor while they walk. In this study, we describe the development of a solution for person verification based on a fused feature selection process inspired by the popular PCA-based eigenfaces approach and a deep learning framework. Dynamic three-dimensional (3D) foot pressure patterns recording during walking were first reduced to ten different 2D pre-feature images. Using the eigenfeet extracted from the peak pressure, a nearest neighbour balanced accuracy (BACC) of 91.1% was obtained based on a single footstep when verifying subjects. Selecting discriminatory eigenfeet, using a minimum-redundancy-maximum-relevance (mRMR), further improved the performance (93.4% BACC), and when fused with a convolutional neural network (CNN) architecture into a stacking PCA network (PCANet+), the maximum verification performance of 96.2% BACC was found. These results show that the proposed selective EigenfeetNet method (i.e., peak pressure, PCANet+, and mRMR) provides a promising platform for the further development of floor sensor-based gait recognition for person verification.
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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 | 0.000 |
| 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) | 0.002 | 0.003 |
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".