A Deep Learning-Based Multimodal Biometric Authentication Framework Using Fingerprint and Iris with Score-Level Fusion
Bibliographic record
Abstract
In today's digital world, there is a growing demand for safe and accurate authentication.This leads biometric authentication to become an important part of an identity verification system.Traditional unimodal biometric verification systems, such as fingerprint, face, or iris recognition, struggle with accuracy due to noisy input images and spoofing attacks.To overcome these issues, this study presents a multimodal biometric recognition framework that combines fingerprint and iris traits using a deep learning approach.Fingerprint features are extracted using a custom Convolutional Neural Network (CNN), while iris features are obtained from a ResNet50 backbone.Each modality is classified independently, and the final identity prediction is produced through score-level fusion of the softmax outputs.The system is evaluated on a multimodal dataset comprising paired fingerprint and iris samples from the same individuals, with an 80:20 train-test split.The unimodal fingerprint and iris classifiers achieved accuracies of 89.4% and 92.1%, respectively, whereas the fused system reached 96.8% with improved precision, recall, and F1-scores.Cross-validation further confirmed the stability of the multimodal results.The findings show that combining complementary biometric traits strengthens recognition performance and reduces the errors observed in unimodal systems, demonstrating the practical advantage of deep learningdriven fusion in biometric authentication.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".