  In this paper, we propose a deep multimodal fusion network to fuse multiple
modalities (face, iris, and fingerprint) for person identification. The
proposed deep multimodal fusion algorithm consists of multiple streams of
modality-specific Convolutional Neural Networks (CNNs), which are jointly
optimized at multiple feature abstraction levels. Multiple features are
extracted at several different convolutional layers from each modality-specific
CNN for joint feature fusion, optimization, and classification. Features
extracted at different convolutional layers of a modality-specific CNN
represent the input at several different levels of abstract representations. We
demonstrate that an efficient multimodal classification can be accomplished
with a significant reduction in the number of network parameters by exploiting
these multi-level abstract representations extracted from all the
modality-specific CNNs. We demonstrate an increase in multimodal person
identification performance by utilizing the proposed multi-level feature
abstract representations in our multimodal fusion, rather than using only the
features from the last layer of each modality-specific CNNs. We show that our
deep multi-modal CNNs with multimodal fusion at several different feature level
abstraction can significantly outperform the unimodal representation accuracy.
We also demonstrate that the joint optimization of all the modality-specific
CNNs excels the score and decision level fusions of independently optimized
CNNs.
