Kinect gait skeletal joint feature-based person identification
Bibliographic record
Abstract
Gait not only defines the way a person walks, but also provides interesting cues on individuals daily routine, mental state, health condition or even cognitive function. The importance of incorporating cognitive behavior and analysis in biometric systems has been noted recently. In this article, we develop a biometric-security system using gait-based skeletal information from Microsoft Kinect v1 sensor. The gait cycle is calculated by detecting the three consecutive local minima between the distance of left and right ankle joints. We have utilized the distance feature vector for each of the joints with respect to other joints in the gait cycle for extraction. Mean and variance features are extracted from the distance feature vector. The K Nearest Neighbors (KNN) algorithm is used for classification purpose. The classification accuracy of our proposed approach is 93.33%. The effectiveness of the method is evaluated by comparing it with others existing approaches. Experimental results show that proposed approach is having better recognition accuracy compared to other approaches. Incorporating this biometric in situation awareness system that can identify the mental state of a human is the future direction of this research.
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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 | 0.000 |
| Research integrity | 0.000 | 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".