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Detection of targets using low power embedded devices has important applications in border security and surveillance. In this paper, we build on recent algorithmic advances in sensor fusion, and present the design and implementation of a novel, multi-mode embedded signal processing system for detection of people and vehicles using acoustic and seismic sensors. Here, by "multi-mode", we mean...
An emerging topic in face recognition is matching between facial images acquired from different sensing modalities, referred to as heterogeneous face recognition. Heterogeneous face recognition has the potential to provide key capabilities for the commercial sector as well as for law enforcement, intelligence gathering, and the military, especially in challenging unconstrained settings. However, the...
In this paper, we develop new multiclass classification algorithms for detecting people and vehicles by fusing data from a multimodal, unattended ground sensor node. The specific types of sensors that we apply in this work are acoustic and seismic sensors. We investigate two alternative approaches to multiclass classification in this context — the first is based on applying Dempster-Shafer Theory...
A method for synthesizing visible spectrum face imagery from polarimetric-thermal face imagery is presented. This work extends recent within-spectrum (i.e., visible-to-visible) reconstruction techniques for image representation understanding using convolutional neural networks. Despite the challenging task, we effectively demonstrate the ability to produce a visible image from a probe polarimetric-thermal...
We present a polarimetric thermal face database, the first of its kind, for face recognition research. This database was acquired using a polarimetric longwave infrared imager, specifically a division-of-time spinning achromatic retarder system. A corresponding set of visible spectrum imagery was also collected, to facilitate crossspectrum (also referred to as heterogeneous) face recognition research...
A face recognition system capable of day- and night-time operation is highly desirable for surveillance and reconnaissance. Polarimetric thermal imaging is ideal for such applications, as it acquires emitted radiation from skin tissue. However, polarimetric thermal facial imagery must be matched to visible face images for interoperability with existing biometric databases. This work proposes a novel...
Several models have been previously suggested for learning correlated representations between source and target modalities. In this paper, we propose a novel coupled autoassociative neural network for learning a target-to-source image representation for heterogenous face recognition. This coupled network is unique, because a cross-modal transformation is learned by forcing the hidden units (latent...
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