Pose invariant face recognition for video surveillance system using kernel principle component analysis

Sepehr Damavandinejadmonfared*, Waled Hussein Al-Arashi, Shahrel Azmin Suandi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Kernel Entropy Component Analysis (KECA) is a newer method than Kernel Principle Component Analysis (KPCA) for data transformation and dimensionality reduction in case of face recognition. Although in almost all previous researches using KECA are shown to be more superior and more appropriate method compared to KPCA, here in this paper the significance of Kernel PCA in handling face pose in surveillance images is compared to KECA. Comparative analysis is made to signify the importance of Kernel Principle Component Analysis in terms of pose invariant face recognition in surveillance.

Original languageEnglish
Title of host publicationFourth International Conference on Digital Image Processing, ICDIP 2012
Pages1-5
Number of pages5
Volume8334
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event4th International Conference on Digital Image Processing, ICDIP 2012 - Kuala Lumpur, Malaysia
Duration: 7 Apr 20128 Apr 2012

Other

Other4th International Conference on Digital Image Processing, ICDIP 2012
CountryMalaysia
CityKuala Lumpur
Period7/04/128/04/12

Keywords

  • face recognition
  • Kernel Entropy Component Analysis (KECA)
  • Kernel Principal Component Analysis (KPCA)

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