Multiview Gait Recognition on Unconstrained Path Using Graph Convolutional Neural Network
Bibliographic record
Abstract
Human gait recognition is a valuable biometric trait with vast applications in security domain. In most situations, the gait data is collected while the subject walks straight. Thus, the performance of the gait recognition system degrades when the subject changes walking direction. Previous gait recognition research was predominantly conducted for constrained paths, which limited the system’s robustness and applicability. This paper introduces a novel approach for gait recognition which aims to recognize subjects walking along an unconstrained path. A graph neural network-based method is proposed for gait recognition along unconstrained path. The input of the architecture is the body joint coordinates and adjacency matrix representing the skeleton joints. Furthermore, a residual connection is incorporated to produce a smoothened output of the input feature. This graph neural network model utilizes the kinematic relationships of the body joints as well as spatial and temporal features. The findings demonstrate that the proposed method outperformed other state-of-the-art gait recognition methods on unconstrained paths. Multi-view Gait AVA and CASIA-B dataset are used to evaluate the efficacy of the proposed method.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".