  When compared to unimodal systems, multimodal biometric systems have several
advantages, including lower error rate, higher accuracy, and larger population
coverage. However, multimodal systems have an increased demand for integrity
and privacy because they must store multiple biometric traits associated with
each user. In this paper, we present a deep learning framework for
feature-level fusion that generates a secure multimodal template from each
user's face and iris biometrics. We integrate a deep hashing (binarization)
technique into the fusion architecture to generate a robust binary multimodal
shared latent representation. Further, we employ a hybrid secure architecture
by combining cancelable biometrics with secure sketch techniques and integrate
it with a deep hashing framework, which makes it computationally prohibitive to
forge a combination of multiple biometrics that pass the authentication. The
efficacy of the proposed approach is shown using a multimodal database of face
and iris and it is observed that the matching performance is improved due to
the fusion of multiple biometrics. Furthermore, the proposed approach also
provides cancelability and unlinkability of the templates along with improved
privacy of the biometric data. Additionally, we also test the proposed hashing
function for an image retrieval application using a benchmark dataset. The main
goal of this paper is to develop a method for integrating multimodal fusion,
deep hashing, and biometric security, with an emphasis on structural data from
modalities like face and iris. The proposed approach is in no way a general
biometric security framework that can be applied to all biometric modalities,
as further research is needed to extend the proposed framework to other
unconstrained biometric modalities.
