3D palmprint recognition using unsupervised convolutional deep learning network and SVM classifier
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
Since past decade, efforts are afoot to design better hand‐based automatic person recognition systems. Among the various hand‐based biometric traits, palmprint as a biometric characteristic is now gaining increased attention from both the academic and industrial communities owing to its highly distinctive texture patterns, features richness, and stability. Here, the authors propose a new 3D palmprint recognition framework based on an unsupervised convolutional deep learning network named PCANet. Specifically, the proposed framework first reconstructs illumination‐invariant 3D palmprint images using Single Scale Retinex (SSR) algorithm. Then, PCANet topology is employed to extract discriminative features from SSR images. Finally, a multi‐class support vector machine (SVM) classification scheme is utilised to determine the identity of the person. Extensive experimental analysis on publicly available 3D palmprint PolyU dataset, which is composed of 8000 range images from 200 individuals, shows that proposed method outperforms existing approaches and is also able to attain 99.98% rank‐1 accuracy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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