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

Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding