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# hw11 - Due Dec 9 2008 4pm CS 257(Luke Olson Homework#11...

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Due: Dec 9, 2008, 4pm CS 257 (Luke Olson): Homework #11 Problem 1 Problem 1 Consider a database of 10 faces in faces.zip . Each is 50 by 50 pixel grayscale in PNG format: person1.png person2.png person3.png person4.png person5.png person6.png person7.png person8.png person9.png person10.png We would like to query this database to see if a person with a disguise of sunglasses ( disguise s.png ) or a musctache ( disguise m.png ) is in there. We will use SVD to look at “eigenfaces” of the database to compare with the disguises. step 1 Read all person * .png files in as 50 by 50 grayscale images and line up in a 2500 by 10 matrix X . step 2 Find the “average” of these faces step 3 Normalize all of the faces in X about this average face step 4 Perform an efficient SVD (not storing zero singular values) on X to generate the eigenfaces (stored in U ). step 5 Use d of the eigenfaces to find a projection of each face onto each of the eigenfaces. w = U(:,1:d)’ * A step 6 Read in a disguise image. step 7 Average this image with the database. step 8 Also project this image onto the eigenfaces. step 9 Compare this project to each of the projections above. That is, compare U(:,1:5)’ * newA to w above. step 10 Determine which face is closest to the disguise. So far, faces.m performs this task. Problem: which disguise is more effective? Using d = 1 is a very cheap comparison. Increasing d quickly becomes much more expensive (if the database is large). So the problem is, which disguise requires more eigenfaces to resolve the match with confidence. What to hand in: a discussion

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hw11 - Due Dec 9 2008 4pm CS 257(Luke Olson Homework#11...

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