paper critique & summarization : Eigenfaces for Recognition

Title : Eigenfaces for Recognition
Author : Matthew Turk and Alex Pentland

summarization:

This paper developed a method to recognize human face based on projecting new faces onto the feature space, rather then depend on the 3-D information or geometry which before researches used, and then called these feature space "eigenface".

The idea is com from a technique developed by Sirovich and Kirby(1987&1990), they represented pictures of faces by using principal component analysis. They calculated a coordinate system for image compression and each coordinate is an image, they called them "eigenpicture".

And this approach has some base intuitions:

1. every human faces have some similarities, like the rough positions of eyes, nose, and mouth.
2. we can compute a average face, which would be the average of all training faces.
3. compute the differ of average face and all training faces, and keeping the M faces which correspond to the highest eigenvaluse, defined them as "face space".
4. then every face could be combined by using this eigenfaces with different weight.


critique:

This paper is similar to the other paper we read this week, they both use some component to combine the original data. And the number of component used also decides how precise the combined data with the original data. And by selecting few principal component, we could get the not bad performance and use little dimension to store the information, to get the effect of dimension reduction we want.