An Efficient Palmprint Authentication System based on One-Class SVM and HOG Descriptor
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Bibliographic record
Abstract
Biometrics characteristics are usually employed in the context of authentication or identification. They are particularly relevant when designing security systems aiming to protect private data and ensure a certain level of access security. Several physiological and behavioural characteristics might be used depending on the type of available sensors. In the context of this paper, we propose a biometric-based authentication system using palmprint as a modality. We introduce the use of one-class classification using a well-known machine learning technique namely the Support Vector Machine classifier during the authentication process. Its main advantage lies in increasing the computational efficiency since each classifier is dedicated to each specific user which makes it independent of the others and reduce the computational load. Moreover, we propose to employ the Histogram of Oriented Gradients along with Principal Component Analysis to generate a relevant and discriminant representation of the palmprint image. We evaluated the proposed biometric-based authentication system with a public benchmark dataset and obtained state-of-the-art performance with 94.67% 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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