A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification
Rudresh Dwivedi, Somnath Dey

TL;DR
This paper introduces a hybrid biometric fusion scheme combining score and decision level methods to enhance cancelable iris and fingerprint verification, improving accuracy and robustness while maintaining privacy.
Contribution
It presents a novel hybrid fusion framework using mean-closure weighting and Dempster-Shafer theory for cancelable multimodal biometric verification.
Findings
Significant performance improvements over unimodal systems.
Robustness to score variability and outliers.
Effective privacy-preserving biometric fusion.
Abstract
In spite of the benefits of biometric-based authentication systems, there are few concerns raised because of the sensitivity of biometric data to outliers, low performance caused due to intra-class variations and privacy invasion caused by information leakage. To address these issues, we propose a hybrid fusion framework where only the protected modalities are combined to fulfill the requirement of secrecy and performance improvement. This paper presents a method to integrate cancelable modalities utilizing mean-closure weighting (MCW) score level and Dempster-Shafer (DS) theory based decision level fusion for iris and fingerprint to mitigate the limitations in the individual score or decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the similarity scores from different matchers corresponding to each protected modality. The individual scores obtained from…
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