Pose invariant face recognition for video surveillance system using kernel principle component analysis
Sepehr Damavandinejadmonfared, Waled Hussein Al-Arashi, Shahrel Azmin Suandi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
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.