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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".