  In this paper, we propose a novel multimodal deep hashing neural decoder
(MDHND) architecture, which integrates a deep hashing framework with a neural
network decoder (NND) to create an effective multibiometric authentication
system. The MDHND consists of two separate modules: a multimodal deep hashing
(MDH) module, which is used for feature-level fusion and binarization of
multiple biometrics, and a neural network decoder (NND) module, which is used
to refine the intermediate binary codes generated by the MDH and compensate for
the difference between enrollment and probe biometrics (variations in pose,
illumination, etc.). Use of NND helps to improve the performance of the overall
multimodal authentication system. The MDHND framework is trained in 3 steps
using joint optimization of the two modules. In Step 1, the MDH parameters are
trained and learned to generate a shared multimodal latent code; in Step 2, the
latent codes from Step 1 are passed through a conventional error-correcting
code (ECC) decoder to generate the ground truth to train a neural network
decoder (NND); in Step 3, the NND decoder is trained using the ground truth
from Step 2 and the MDH and NND are jointly optimized. Experimental results on
a standard multimodal dataset demonstrate the superiority of our method
relative to other current multimodal authentication systems
