Static Hand Gesture Angle Recognition via Aggregated Channel Features (ACF) Detector
Bibliographic record
Abstract
Static hand gesture recognition is critical in the development of a system for human-computer interaction. Many human-computer interactions, such as human-robot interaction, game control, control of smart home devices, and others, use hand gestures as a fundamental and natural language of the body. The direction of rotation of static hand gestures is the subject of this research, and the focus is on six degrees of rotation (0°, 45°, 90°, 180°, 270°, and 315°). This work presents an ideal approach that can recognize the angle of hand gestures based on the Aggregate Channel Features (ACF) detector. This approach consists of three main stages: preprocessing (image labelling), computer training, and hand angle detection based on the ACF detector. The training process consists of 25 stages. The static hand gesture dataset contained 569 images (361 for training and 208 for testing). The average time cost to detect all hand gesture angles was 0.9445 seconds, and all hand angles were recognized with 100% accuracy. This is a strong indication that supports our approach.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".