Due to the rapid growth of biometric technology, template protection becomes crucial to secure integrity of the biometric
security system and prevent unauthorized access. Cancelable biometrics is emerging as one of the best solutions to secure
the biometric identification and verification system. We present a novel technique for robust cancelable template
generation algorithm that takes advantage of the multimodal biometric using feature level fusion. Feature level fusion of
different facial features is applied to generate the cancelable template. A proposed algorithm based on the multi-fold
random projection and fuzzy communication scheme is used for this purpose. In cancelable template generation, one of
the main difficulties is keeping interclass variance of the feature. We have found that interclass variations of the features
that are lost during multi fold random projection can be recovered using fusion of different feature subsets and projecting
in a new feature domain. Applying the multimodal technique in feature level, we enhance the interclass variability hence
improving the performance of the system. We have tested the system for classifier fusion for different feature subset and
different cancelable template fusion. Experiments have shown that cancelable template improves the performance of the
biometric system compared with the original template.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.