Dynamic ROI Extraction for Palmprints using MediaPipe Hands
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
Hand-based biometric traits have been widely used in recognition systems. Dynamic region of interest extraction is an important preprocessing step for these systems to avoid recognition performance degradation. In this study, a dynamic region of interest extraction method that can be used for palm vein, palmprint, and dorsal hand vein has been proposed using Google’s MediaPipe Hands framework. Since 3 biometric traits focus on nearly the same region that contains biometric information on the images, this study aims to show that the proposed extraction method can be utilized for these traits on mobile biometric applications. This method has been implemented on IIT Delhi Touchless Palmprint Database and 93% accuracy was obtained. The average processing time per image for ROI extraction was recorded as 2.64 seconds. With this study, a paradigm for future studies on hand biometrics has been created and the required processing time for a dynamic extraction has been reduced considerably.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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