Chinese sign language recognition with 3D hand motion trajectories and depth images
Why this work is in the frame
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Bibliographic record
Abstract
An important part for sign language expression is hand shape, and the 3D hand motion trajectories also contain abundant information to interpret the meaning of sign language. In this paper, a novel feature descriptor is proposed for sign language recognition, the hand shape features extracted from the depth images and spherical coordinate (SPC) feature extracted from the 3D hand motion trajectories combine to make up the final feature representation. The new representation not only incorporates both the spatial and temporal information to depict the kinematic connectivity among hand, wrist and elbow for recognition effectively but also avoids the interference of the illumination change and cluttered background compared with other methods. Meanwhile, our self-built dataset includes 320 instances to evaluate the effectiveness of our combining feature. In experiments with the dataset and different feature representation, the superior performance of Extreme Learning Machine (ELM) is tested, compared with Support Vector Machine (SVM).
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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 | 0.001 |
| 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 it