  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 different matchers for each
modality are combined using MCW score fusion method. The MCW technique achieves
the optimal weight for each matcher involved in the score computation. Further,
DS theory is applied to the induced scores to output the final decision. The
rigorous experimental evaluations on three virtual databases indicate that the
proposed hybrid fusion framework outperforms over the component level or
individual fusion methods (score level and decision level fusion). As a result,
we achieve (48%,66%), (72%,86%) and (49%,38%) of performance improvement over
unimodal cancelable iris and unimodal cancelable fingerprint verification
systems for Virtual_A, Virtual_B and Virtual_C databases, respectively. Also,
the proposed method is robust enough to the variability of scores and outliers
satisfying the requirement of secure authentication.
