MACET: A Novel Approach to Secure Multimodal Biometric Authentication with Cancellable Templates
Bibliographic record
Abstract
Biometric authentication is a cornerstone of modern security systems, yet concerns regarding privacy and data security persist. Cancellable biometrics offer a solution by transforming raw biometric data into non-invertible representations, ensuring security even in the event of a data breach. This study presents Multimodal Affine Cover-space Euler Transformation (MACET), a novel framework designed to enhance biometric template security while preserving authentication accuracy. The proposed approach is based on the hypothesis that Affine Cover Space transformation combined with Euler’s form can generate irreversible templates for multimodal biometrics, specifically fingerprint and iris data, without compromising recognition performance. The methodology involves feature extraction, inverse matrix computation, affine transformation, and Euler-based augmentation, ensuring robust and secure biometric template generation. Experimental results, conducted on a dataset of 450 biometric samples, demonstrate the effectiveness of MACET in improving authentication performance. The system achieves an Equal Error Rate (EER) of 0.0046 and an Area Under the ROC Curve (AROC) of 0.9886, indicating high accuracy. Additionally, the method significantly reduces storage memory size to 1.37 KB per template while maintaining an average execution time of 10.89 seconds. Robustness analysis against spoofing attacks confirms the system's ability to resist unauthorized access, ensuring strong security and privacy protection. These findings establish MACET as a highly secure, computationally efficient, and privacy-preserving biometric authentication framework, suitable for real-world applications. Future research could extend this approach to additional biometric modalities and large-scale authentication systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".