Face hallucination through KPCA

Yan Liang, Jian Huang Lai*, Yao Xian Zou, Wei Shi Zheng, Pong Chi YUEN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

This paper demonstrates how Kernel Principal Component Analysis (KPCA) can be used for face hallucination. Different with other KPCA-based methods, KPCA in this paper handles samples from two subspaces, namely the high- and low-resolution image spaces. As KPCA learns not only linear features but also non-linear features, it is anticipated that more detailed facial features could be synthesized. We propose a new model and give theoretical analysis on when it is applicable. Algorithm is then developed for implementation. Experiments are conducted to compare the proposed method with the existing well-known face hallucination methods in terms of visual quality and mean square error. Our results are better and encouraging.

Original languageEnglish
Title of host publicationProceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09
DOIs
Publication statusPublished - 2009
Event2009 2nd International Congress on Image and Signal Processing, CISP'09 - Tianjin, China
Duration: 17 Oct 200919 Oct 2009

Publication series

NameProceedings of the 2009 2nd International Congress on Image and Signal Processing, CISP'09

Conference

Conference2009 2nd International Congress on Image and Signal Processing, CISP'09
Country/TerritoryChina
CityTianjin
Period17/10/0919/10/09

Scopus Subject Areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering

User-Defined Keywords

  • Face hallucination
  • KPCA
  • Pre-image

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