Palmprint Biometrics: Online Learning with Differential Evolution and Contrastive Representation
Bibliographic record
Abstract
Contactless palmprint biometrics offer a promising solution for mobile authentication with distinctive features of palmprints, such as principal lines and wrinkles. However, a significant challenge arises with the incremental class problem, as users can register their palmprints on their mobile devices at any time without centralized control. This dynamic enrollment creates difficulties in integrating new classes without degrading the system’s performance on existing classes. To address this, our paper present an online evolutive learning approach that combines contrastive learning with a modified differential evolution algorithm, allowing the system to efficiently incorporate new biometric data without necessitating complete model retraining. Utilizing Siamese networks, we develop robust embedding representations that facilitate accurate user registration and authentication. Evaluations on the 11 k Hands dataset demonstrate that our approach significantly outperforms traditional fine-tuning methods, achieving higher accuracy, precision, recall, and F1 score as the number of classes increases. These results highlight the efficacy and practicality of our solution for real-time biometric systems, providing enhanced security and adaptability for mobile applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".