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Face Recognition using Independent Component Analysis ...
Principal Component Analysis is used for the Face Recognition System [13]. This research used a version of PCA for facial images in FERET database where input picture is treated as random ...
Face Recognition Using Independent Component Analysis and ...
Deniz et al. 3 developed face recognition using independent component analysis (ICA) and SVM. They concluded that the best practical combination for face recognition is PCA with SVM on the face ...
 Independent Component Representations for Face Recognition
Keywords: Independent component analysis, ICA, principal component analysis, PCA, face recognition. 1. INTRODUCTION Several advances in face recognition such as "H~lons,~ " "Eigenfa~es,~ " and "Local Feature Analysis4" have employed forms of principal component analysis, which addresses only second-order moments of the input. Principal component...
Face recognition using Principal Component Analysis
Facial recognition can be done by various methods such as principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA), a local binary pattern ...
ICA Face Recognition Matlab code - YouTube
A number of face recognition algorithms employ principal component analysis (PCA), which is based on the second-order statistics of the image set, and does not address high-order statistical ...
Principal Manifolds and Probabilistic Subspaces for Visual ...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and nonlinear Kernel PCA (KPCA) are examined and tested in a visual recognition experiment using 1,800+ facial images from the "FERET"database....
A Case for the Average-Half-Face in 2D and 3D for Face ...
3. Face Recognition Algorithms We will briefly discuss each of the 6 face recognition methods used in our experiments. 3.1. Eigenfaces Eigenfaces was introduced early [15] on as powerful use of principal components analysis (PCA) to solve problems in face recognition and detection. Eigenfaces is a subspace...
Independent Principal Component Analysis for biologically ...
Independent Principal Component Analysis (IPCA) makes the assumption that biologically meaningful components can be obtained if most noise has been removed in the associated loading vectors. In IPCA, PCA is used as a pre-processing step to reduce the dimension of the data and to generate the loading vectors.
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