Principal Component Analysis (PCA)
Principal Component Analysis (PCA)
Principal Component Analysis (PCA)
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Notes:<br />
• not surprisingly, the approximation of the reconstructed<br />
faces get better when more eigenfaces are used<br />
• In the extreme case of using no eigenface at all, the<br />
reconstruction of any face image is just the average<br />
face<br />
• In the other extreme of using all 97 eigenfaces, the<br />
reconstruction is exact, since the image was in the<br />
original training set of 97 images. For a new face image,<br />
however, its reconstruction based on the 97 eigenfaces<br />
will only approximately match the original.<br />
• What regularities does each eigenface capture? See<br />
movie.<br />
Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 34
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