Multispectral hand recognition using the Kinect v2 sensor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multispectral data from inexpensive, yet accurate, sensors has become readily available within the last several years and opened many possibilities for contactless biometrics applications. The Kinect v2 provides depth, RGB, and Near-Infrared (NIR) data and can be used for recognition of individuals using extracted hand regions in all three spectra. Initially, the depth data is used to extract the hand region for use as a mask to extract the hand region in the depth, RGB, and Near-Infrared (NIR) spectra. These extracted regions then have Principal Component Analysis (PCA) applied to them before passing through classification. K-Nearest-Neighbors (KNN) and Support Vector Machines (SVM) are compared for classification. In testing it was found that on average the RGB and NIR data provided a recognition rate of approximately 75%-80% for either KNN or SVM classification and at different amounts of principal components for PCA.
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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 | 0.000 |
| 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