Global face reconstruction for face hallucination using orthogonal canonical correlation analysis

Huiling Zhou, Jiwei Hu, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

In this paper, a global face reconstruction framework for face hallucination is proposed to globally reconstruct a high-resolution (HR) version of a face from an input low-resolution (LR) face, based on learning from LR-HR face pairs using orthogonal canonical correlation analysis (orthogonal CCA). In our proposed algorithm, face images are first represented using principal component analysis (PCA). CCA with the orthogonality property is then employed to maximize the correlation between the PCA coefficients of the LR and the HR face pairs so as to improve the hallucination performance. The original CCA does not own the orthogonality property, which is crucial for information reconstruction. In this paper, we utilize an orthogonal variant of CCA, which has been proven by experiments to achieve a better performance than the original CCA in terms of global face reconstruction.
Original languageEnglish
Title of host publication2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
PublisherIEEE
Pages537-542
Number of pages6
ISBN (Electronic)9789881476807
DOIs
Publication statusPublished - 19 Feb 2016
Event2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong
Duration: 16 Dec 201519 Dec 2015

Conference

Conference2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
CountryHong Kong
CityHong Kong
Period16/12/1519/12/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modelling and Simulation
  • Signal Processing

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