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

Sepehr Damavandinejadmonfared; Waled Hussein Al-Arashi; Shahrel Azmin Suandi

doi:10.1117/12.956494

8 June 2012 Pose invariant face recognition for video surveillance system using kernel principle component analysis

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

Proceedings Volume 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012); 833439 (2012) https://doi.org/10.1117/12.956494
Event: Fourth International Conference on Digital Image Processing (ICDIP 2012), 2012, Kuala Lumpur, Malaysia

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.

Citation Download Citation

Sepehr Damavandinejadmonfared, Waled Hussein Al-Arashi, and Shahrel Azmin Suandi "Pose invariant face recognition for video surveillance system using kernel principle component analysis", Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 833439 (8 June 2012); https://doi.org/10.1117/12.956494

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