Robust face recognition based on a new supervised kernel subspace learning method
A Khalili Mobarakeh, JA Cabrera Carrillo… - Sensors, 2019 - mdpi.com
Face recognition is one of the most popular techniques to achieve the goal of figuring out the
identity of a person. This study has been conducted to develop a new non-linear subspace …
identity of a person. This study has been conducted to develop a new non-linear subspace …
Finger vein recognition using linear kernel entropy component analysis
S Damavandinejadmonfared - 2012 IEEE 8th International …, 2012 - ieeexplore.ieee.org
Based on the previous research, Kernel Entropy Component Analysis (KECA) is introduced
as a more appropriate method than Kernel Principal Component Analysis (KPCA) for face …
as a more appropriate method than Kernel Principal Component Analysis (KPCA) for face …
[PDF][PDF] Finger vein recognition using PCA-based methods
S Damavandinejadmonfared… - World Academy of …, 2012 - academia.edu
In this paper a novel algorithm the accuracy of finger vein recognition. T Principal
Component Analysis (PCA), Kernel Analysis (KPCA), and Kernel Entropy Compon in this …
Component Analysis (PCA), Kernel Analysis (KPCA), and Kernel Entropy Compon in this …
[PDF][PDF] Using Linear Kernel Entropy Component Analysis as a Feature Extraction Method in Face Recognition in video surveillance systems
S Damavandinejadmonfared… - Proceedings of the …, 2012 - world-comp.org
Kernel Entropy Component Analysis (KECA) is one of the latest improvements on Principal
Component Analysis (PCA) and also Kernel Principal Component Analysis (KPCA). As …
Component Analysis (PCA) and also Kernel Principal Component Analysis (KPCA). As …