Hand posture recognition using K-NN and Support Vector Machine classifiers evaluated on our proposed HandReader dataset
Bibliographic record
Abstract
In this paper, we propose a real-time vision-based hand posture recognition approach, based on appearance-based features of the hand poses. Our approach has three main steps: Preprocessing, Feature Extraction and Posture Recognition. Additionally, a new hand posture dataset called HandReader is created and introduced. HandReader is a dataset of 500 images of 10 different hand postures which are 10 non-motion-based American Sign Language alphabets with dark backgrounds. The dataset is gathered by capturing images of 50 male and female individuals performing these 10 hand postures in front of a common camera. 20% of the HandReader images are used for the training purpose and the remaining 80% are used to test the proposed methodology. All the images are normalized after applying the preprocessing step. The normalized images are then converted to feature vectors in the Feature Extraction step. In order to train the system, k-NN classifier and SVM classifiers with linear and RBF kernel have been employed and results were compared. These approaches were used to classify hand posture images into 10 different posture classes. The SVM classifier with linear kernel performed better with the highest true detection rate (96%) among other proposed techniques.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".