Palmprint Biometrics: Online Learning with Differential Evolution and Contrastive Representation
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
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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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 | 0.003 |
| 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