AI-Driven Secure Authentication: A Deep Learning Approach for Multi-Modal Biometric Systems
Bibliographic record
Abstract
Biometric authentication technologies have become quite popular owing to their great accuracy and security. However, specific biometric features such as face, hand, and iris pictures often have limits when utilized alone. To address these problems, in this study offer the MMFusion-Net model, which combines three biometric features (facial, hand, and iris) using a Deep Learning-based fusion technique. The model blends cuttingedge Convolutional Neural Networks (CNNs) with innovative fusion processes to increase biometric identification systems' accuracy and dependability. MMFusion-Net outperforms standard models such as SVM, MLP, CNN, and others in terms of accuracy, precision, recall, and F1-score. The model's efficacy was further verified using comprehensive hyperparameter tweaking and ablation tests, which confirmed the importance of multimodal fusion and deep learning architectures. The findings emphasize the need of integrating different biometric features to create strong and secure identification systems. MMFusion-Net outperforms previous approaches and establishes itself as a potential alternative for future biometric 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".