Gabor-Feature Hallucination based on Generalized Canonical Correlation Analysis for face recognition

Kuong Hon Pong, Kin Man Lam

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

3 Citations (Scopus)


In face recognition, image resolution is an important factor which has a great influence on the recognition rate. In traditional face recognition for low-resolution images, face interpolation/super-resolution is usually performed first, and the constructed high-resolution face image will then pass through a face recognition system, which includes feature extraction and classification. To achieve a more efficient and accurate approach, we propose a new method of Gabor-Feature Hallucination, which predicts the high-resolution Gabor features from the low-resolution Gabor features directly by using linear regression and Generalized Canonical Correlation Analysis. Then, the low-resolution features in the projected Generalized Canonical Correlation space and the predicted high-resolution Gabor features are adopted for face classification. Our algorithm can therefore avoid performing interpolation/super-resolution and high-resolution Gabor feature extraction. Experimental results show that the proposed method has a superior recognition rate and efficiency to the traditional methods.
Original languageEnglish
Title of host publication2011 International Symposium on Intelligent Signal Processing and Communications Systems
Subtitle of host publication"The Decade of Intelligent and Green Signal Processing and Communications", ISPACS 2011
Publication statusPublished - 1 Dec 2011
Event19th IEEE International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2011 - Chiang Mai, Thailand
Duration: 7 Dec 20119 Dec 2011


Conference19th IEEE International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2011
CityChiang Mai


  • face recognition
  • Gabor feature
  • generalized canonical correlation analysis
  • hallucination
  • linear regression

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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