Biometric User Authentication System via Fingerprints Using Novel Hybrid Optimization Tuned Deep Learning Strategy
Bibliographic record
Abstract
Due to security concerns, the necessity for authentication and identity techniques has increased in the modern world.A novel Accurate and Automated Fingerprint Biometric Authentication Model (AAFBAM) is introduced.The operation of the suggested AAFBAM is divided into two parts: (a) enrollment and (b) verification.During the enrollment phase, the database is prepared, and the input fingerprint is authenticated during the identification phase.The enrollment phase includes the data acquisition stage, preprocessing feature extraction stage, and minutiae point detection phase.The minutiae point detection is performed using the MISHO-based Optimized Deep Neural Network (MISHO-DNN) classifier.The weight function of DNN is tuned optimally using the proposed Memory Integrated Spotted Hyena Optimization (MISHO) algorithm to enhance its detection accuracy.The Verification Phase includes the preprocessing, feature extraction stage, minutiae point detection with MISHO-DNN, minutia matching, and minutiae score evaluation.Here, the minutiae score is obtained by matching minutiae from both phases and is compared with the threshold value.When the minutiae score exceeds the threshold, the user is identified as the genuine user, and their request is accepted.Otherwise, the user is recognized as an unauthenticated user, and their request is rejected.Finally, a comparative evaluation is conducted to validate the efficiency of the projected model.
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How this classification was reachedexpand
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 | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".