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Multi-Resolution Patch Tensor for Facial Expression Hallucination

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Multi-Resolution Patch Tensor for Facial Expression Hallucination
In this paper, we propose a sequential approach to hallucinate/synthesize high-resolution images of multiple facial expressions. We propose an idea of multi-resolution tensor for super-resolution, and decompose facial expression images into small local patches. We build a multi-resolution patch tensor across different facial expressions. By unifying the identity parameters and learning the subspace mappings across different resolutions and expressions, we simplify the facial expression hallucination as a problem of parameter recovery in a patch tensor space. We further add a high-frequency component residue using nonparametric patch learning from high-resolution training data. We integrate the sequential statistical modelling into a Bayesian framework, so that given any low-resolution facial image of a single expression, we are able to synthesize multiple facial expression images in high-resolution. We show promising experimental results from both facial expression database and live v...
Kui Jia, Shaogang Gong
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Kui Jia, Shaogang Gong
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