Generalized face super-resolution.

Full Abstract

Existing learning-based face super-resolution (hallucination) techniques generate high-resolution images of a single facial modality (i.e., at a fixed expression, pose and illumination) given one or set of low-resolution face images as probe. Here, we present a generalized approach based on a hierarchical tensor (multilinear) space representation for hallucinating high-resolution face images across multiple modalities, achieving generalization to variations in expression and pose. In particular, we formulate a unified tensor which can be reduced to two parts:
a global image-based tensor for modeling the mappings among different facial modalities, and a local patch-based multiresolution tensor for incorporating high-resolution image details. For realistic hallucination of unregistered low-resolution faces contained in raw images, we develop an automatic face alignment algorithm capable of pixel-wise alignment by iteratively warping the probing face to its projection in the space of training face images. Our experiments show not only performance superiority over existing benchmark face super-resolution techniques on single modal face hallucination, but also novelty of our approach in coping with multimodal hallucination and its robustness in automatic alignment under practical imaging conditions.

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Author information

Author/s: Jia, Kui (K); Gong, Shaogang (S);

Affiliation: Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences/Chinese Academy of Hong Kong, Shenzhen, China. chrisjia(-atsign-)dcs.qmul.ac.uk

Journal and publication information

Publication Type: Journal Article

Journal: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society (IEEE Trans Image Process), published in United States. (Language: eng)

Reference: 2008-Jun; vol 17 (issue 6) : pp 873-86

Dates: Created 2008/05/16; Completed 2008/06/19;

PMID: 18482883, status: MEDLINE (last retrieval date: 11/6/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Biometry - methods
Face - anatomy & histology
Humans
Image Enhancement - methods

Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique

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