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The face recognition problem is difficult by the great change in facial expression, head rotation and tilt, lighting intensity and angle, aging, partial occlusion (e.g. Wearing Hats, scarves, glasses, etc.), etc. The Eigenfaces algorithm has long been a mainstay in the field of face recognition and the face space has high dimension. Principal components from the face space are used for face recognition to reduce dimensionality. In this paper, the technique PCA is applied to find the face recognition accuracy rate and Kernel PCA is described.
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