Dynamic Descriptors in Human Gait Recognition
Bibliographic record
Abstract
Feature extraction is the most critical step in any human gait recognition system. Although gait is a dynamic process yet the static body parameters also play an important role in characterizing human gait. A few studies were performed in the past to assess the comparative relevance of static and dynamic gait features. There is, however, a lack of work in comparative performance analysis of dynamic gait features from different parts of the silhouettes in an appearance based setup. This dissertation presents a comparative study of dynamic features extracted from legs, arms and shoulders for gait recognition. Our study partially supports the general notion of leg motion being the most important determining factor in gait recognition. But it is also observed that features extracted from upper arm and shoulder area become more significant in some databases. The usefulness of the study hinges on the fact that lower parts of the leg are generally more noisy due to a variety of variations such as walking surface, occlusion and shadows. Dynamic features extracted from the upper part of the silhouettes posses significantly higher discriminatory power in such situations. In other situations these features can play a complementary role in the gait recognition process. We also propose two new feature extraction methods for gait recognition. The new methods use silhouette area signals which are easy and simple to extract. A significant performance increase is achieved by using the new features over the benchmark method and recognition results compare well to the other current techniques. The simplicity and compactness of the proposed gait features is their major advantage because it entails low computational overhead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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; both teacher heads agree on what is shown here.
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".