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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. 

In this paper, we propose a new Automatic Target Recognition (ATR) system, based on Deep Convolutional Neural Network (DCNN), to detect the targets in Forward Looking Infrared (FLIR) scenes and recognize their classes. In our proposed ATR framework, a fully convolutional network (FCN) is trained to map the input FLIR imagery data to a fixed stride correspondingly-sized target score map. The potential targets are identified by applying a threshold on the target score map. Finally, corresponding regions centered at these target points are fed to a DCNN to classify them into different target types while at the same time rejecting the false alarms. The proposed architecture achieves a significantly better performance in comparison with that of the state-of-the-art methods on two large FLIR image databases. 

Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry. Despite the significant improvements in sketch-to-photo synthesis techniques, existing methods have still serious limitations in practice, such as the need for paired data in the training phase or having no control on enforcing facial attributes over the synthesized image. In this work, we present a new framework, which is a conditional version of Cycle-GAN, conditioned on facial attributes. The proposed network forces facial attributes, such as skin and hair color, on the synthesized photo and does not need a set of aligned face-sketch pairs during its training. We evaluate the proposed network by training on two real and synthetic sketch datasets. The hand-sketch images of the FERET dataset and the color face images from the WVU Multi-modal dataset are used as an unpaired input to the proposed conditional CycleGAN with the skin color as the controlled face attribute. For more attribute guided evaluation, a synthetic sketch dataset is created from the CelebA dataset and used to evaluate the performance of the network by forcing several desired facial attributes on the synthesized faces.