Eigenface

From Wikipedia, the free encyclopedia

Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. These eigenvectors are derived from the covariance matrix of the probability distribution of the high-dimensional vector space of possible faces of human beings.

Many authors prefer the term eigenimage rather than eigenface, as the technique has been used for handwriting, lip reading, voice recognition, and medical imaging.

Some eigenfaces from AT&T Laboratories Cambridge.

In layman's terms, eigenfaces are a set of "standardized face ingredients", derived from statistical analysis of many pictures of faces. Any human face can be considered to be a combination of these standard faces. One person's face might be made up of 10% from face 1, 24% from face 2 and so on. This means that if you want to record someone's face for use by face recognition software you can use far less space than would be taken up by a digitised photograph.

To generate a set of eigenfaces, a large set of digitized images of human faces, taken under the same lighting conditions, are normalized to line up the eyes and mouths. They are then all resampled at the same pixel resolution (say m×n), and then treated as mn-dimensional vectors whose components are the values of their pixels. The eigenvectors of the covariance matrix of the statistical distribution of face image vectors are then extracted. It should be noted that these are the same as the eigenvectors from principal components analysis, the statistical method from which eigenimaging is derived.

Since the eigenvectors belong to the same vector space as face images, they can be viewed as if they were m×n pixel face images: hence the name eigenfaces.

Viewed in this way, the principal eigenface looks like a bland androgynous average human face. Some subsequent eigenfaces can be seen to correspond to generalized features such as left-right and top-bottom asymmetry, or the presence or absence of a beard. Other eigenfaces are hard to categorize, and look rather strange.

When properly weighted, eigenfaces can be summed together to create an approximate gray-scale rendering of a human face. Remarkably few eigenvector terms are needed to give a fair likeness of most people's faces, so eigenfaces provide a means of applying data compression to faces for identification purposes.

[edit] References

H. Abdi (1988). “A generalized approach for connectionist auto-associative memories: interpretation, implications and illustration for face processing”, J. Demongeot (Ed.): Artificial Intelligence and Cognitive Sciences. Manchester: Manchester University Press, 149–165.
L. Sirovich and M. Kirby (1987). "Low-dimensional procedure for the characterization of human faces". Journal of the Optical Society of America A 4: 519–524.
M. Kirby and L. Sirovich (1990). "Application of the Karhunen-Loeve procedure for the characterization of human faces". IEEE Transactions on Pattern analysis and Machine Intelligence 12 (1): 103–108.
M. Turk and A. Pentland (1991). "Face recognition using eigenfaces". Proc. IEEE Conference on Computer Vision and Pattern Recognition, 586–591.
M. Turk and A. Pentland (1991). "Eigenfaces for recognition". Journal of Cognitive Neuroscience 3 (1): 71–86.
A. Pentland, B. Moghaddam, T. Starner, O. Oliyide, and M. Turk. (1993). "View-based and modular Eigenspaces for face recognition". Technical Report 245, M.I.T Media Lab.
Valentin, D., Abdi, H., O'Toole, A.J., Cottrell, G.W. (1994). "Connectionist models of face processing: A survey.". Pattern Recognition 27: 1208–1230.