Hallucinating faces: TensorPatch super-resolution and coupled residue compensation

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 2; pp. 478 - 484 vol. 2
• Main Authors: Wei Liu, Dahua Lin, Xiaoou Tang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Algebra; Asia; Face detection; Humans; Image analysis; Image resolution; Inference algorithms; Principal component analysis; Rendering (computer graphics); Tensile stress
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2005.172

In this paper, we propose a new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models. Considering that image patches reflect the combined effect of personal characteristics and patch-location, we first formulate a TensorPatch model based on multilinear analysis to explicitly model the interaction between multiple constituent factors. Motivated by locally linear embedding, we develop an enhanced multilinear patch hallucination algorithm, which efficiently exploits the local distribution structure in the sample space. To better preserve face subtle details, we derive the coupled PCA algorithm to learn the relation between high-resolution residue and low-resolution residue, which is utilized for compensate the error residue in hallucinated images. Experiments demonstrate that our framework on one hand well maintains the global facial structures, on the other hand recovers the detailed facial traits in high quality.