Description: 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.
- [FisherFace] - an efficient fisher face recognition sou
File list (Check if you may need any files):
fisherfaces programme\cvKnn.m
.....................\cvLda.m
.....................\cvLdaInvProj.m
.....................\cvLdaProj.m
.....................\cvPca.m
.....................\cvPcaInvProj.m
.....................\cvPcaProj.m
.....................\Eigenface.m
.....................\Fisherface.m
fisherfaces programme
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