Artificial Neural Network Based Gait Recognition Using Kinect Sensor
Bibliographic record
Abstract
Accurate gait recognition is of high significance for numerous industrial and consumer applications, including video surveillance, virtual reality, on-line games, medical rehabilitation, collaborative space exploration, and others. This paper proposes a new architecture designed using deep learning neural network for a highly accurate and robust Kinect-based gait recognition. Two new geometric features: joint relative cosine dissimilarity and joint relative triangle area are introduced. Both of the proposed features are view and pose invariant, thus enhancing recognition performance. The proposed neural network model is trained using the feature vector of dynamic joint relative cosine dissimilarity and joint relative triangle area. Subsequent application of Adam optimization method minimizes the loss of the objective function iteratively. The performance of the proposed deep learning neural network architecture is evaluated on two publicly available 3D skeleton-based gait datasets recorded with the Microsoft Kinect sensor. It is experimentally proven that the accuracy, precision, recall, and F-score of the proposed neural network architecture, trained using introduced dynamic geometric features, is superior to other state-of-the-art methods for Kinect skeleton-based gait recognition.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".