Eigenfaces for Reconition

"Eigenfaces for recognition," M Turk, A Pentland - Journal of Cognitive Neuroscience, 1991.




This paper propose a feature for dimension reduction. The significant features are known as "eigenfaces", and the center idea is to project face images onto the eigenfaces based on PCA. The main procedure is:

1. Given a set of face images :
2. Define the average face :
3. Each face differs from the average face :
4. Construct the M by M matrix L = A'A in replace of the covariance matrix C since there are at most M-1 meaningful eigenfaces, where M is the number of training data and
5. Now we can use L to find eigenfaces :

I think the "eigenfaces" is an intelligent and interesting idea in 1990s like today's SIFT and visual words, even though the times that computer is not so powerful nowadays. Although the approach is not work with unclear face images, it still gives us a idea for handling today's multimedia data.