Sample Size in Floor Sensor-Based Gait Recognition for Smart Home and Access Control Scenarios
Bibliographic record
Abstract
Floor sensor-based gait recognition is an emerging biometric technology that can be used in a variety of scenarios ranging from access control to personalized recognition for smart home systems. These distinct scenarios can differ based on: (1) the number of users in the system, N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user</inf>, and (2) the number of training footstep samples per user made available during the enrollment process, N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">step</inf>. In this study, the effect of these two parameters on the accuracy of state-of-the-art machine learning (ML) and deep learning (DL) models was investigated. For the smart home scenario (small N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user</inf>, large N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">step</inf>), a best person verification performance of 99.77% accuracy was found using a lightweight convolutional neural network (CNN) model with spatial image features. For the access control scenario (large N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user</inf>, small N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">step</inf>), on the other hand, a highest accuracy of 90.70% was found using a shallow k-nearest neighbor classifier with spatiotemporal information, and not end-to-end CNNs or transfer learning based on a pre-trained ResNet-50. Finally, a learning curve analysis is conducted to inform how many training footsteps are needed for both ML and DL approaches to person verification based on different numbers of users.
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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) | 0.000 | 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 teacher head, 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".