fisherface
于 2010-11-20 发布
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代码说明：
Eigenfaces: PCA tends to find a p-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space (p N). We called the new subspace defined by basis vectors “face space”. First, all training faces are projected onto the face space to find a set of weights that describes the contribution of each vector. Then we project all testing faces onto the face space to obtain a set of weights. Finally, we identify the face by comparing a set of weights for the testing face to sets of weights of training faces.
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