Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61854
Title: Face hallucination using orthogonal canonical correlation analysis
Authors: Zhou, H
Lam, KM 
Keywords: Canonical correlation analysis
Face hallucination
Linear mapping
Orthogonal canonical correlation analysis
Super-resolution
Issue Date: 2016
Publisher: SPIE-International Society for Optical Engineering
Source: Journal of electronic imaging, 2016, v. 25, no. 3, 033005 How to cite?
Journal: Journal of electronic imaging 
Abstract: A two-step face-hallucination framework is proposed to reconstruct a high-resolution (HR) version of a face from an input low-resolution (LR) face, based on learning from LR-HR example face pairs using orthogonal canonical correlation analysis (orthogonal CCA) and linear mapping. In the proposed algorithm, face images are first represented using principal component analysis (PCA). Canonical correlation analysis (CCA) with the orthogonality property is then employed, to maximize the correlation between the PCA coefficients of the LR and the HR face pairs to improve the hallucination performance. The original CCA does not own the orthogonality property, which is crucial for information reconstruction. We propose using orthogonal CCA, which is proven by experiments to achieve a better performance in terms of global face reconstruction. In addition, in the residual-compensation process, a linear-mapping method is proposed to include both the inter-and intrainformation about manifolds of different resolutions. Compared with other state-of-the-art approaches, the proposed framework can achieve a comparable, or even better, performance in terms of global face reconstruction and the visual quality of face hallucination. Experiments on images with various parameter settings and blurring distortions show that the proposed approach is robust and has great potential for real-world applications.
URI: http://hdl.handle.net/10397/61854
ISSN: 1017-9909
EISSN: 1560-229X
DOI: 10.1117/1.JEI.25.3.033005
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