Multi-Resolution Patch Tensor for Facial Expression Hallucination

Authors:
Kui Jia;Shaogang Gong
Affiliations:
Queen Mary University of London;Queen Mary University of London
Venue:
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Year:
2006

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Abstract

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 video sequences.