Christopher Reale

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Heterogeneous face recognition is the problem of identifying a person from a face image acquired with a nontraditional sensor by matching it to a visible gallery. Most approaches to this problem involve modeling the relationship between corresponding images from the visible and sensing domains. This is typically done at the patch level and/or with shallow(More)
In recent years, state-of-the-art face recognition performance has improved by using deep convolutional neural networks. One disadvantage of these methods is their need for very large, labeled training datasets as collecting and labeling them can be time consuming and prone to error. In this work we examine the robustness of a convolutional neural network(More)
Thermal to visible face recognition is the problem of identifying a thermal infrared (IR) face image given a gallery of visible light face images. We attempt to solve this problem by learning coupled dictionaries to represent the two domains. The dictionaries provide a sparse representation which transforms the data into a single, domain-independent, latent(More)
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