EigenFace
Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face classification. It is considered the first successful example of facial recognition technology. These eigenvectors are derived from the covariance matrix of the probability distribution of the high-dimensional vector space of possible faces of human beings.
How To Generate EigenFace?
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. Eigenfaces can be extracted out of the image data by means of a mathematical tool called principal component analysis (PCA). Here are the steps involved in converting an image of a face into eigenfaces:
- Prepare a training set T. The faces constituting the training set T should be already prepared for processing.
- Subtract the mean. The average matrix A has to be calculated and subtracted from the original in T. The results are stored in variable S.
- Calculate the covariance matrix.
- Calculate the eigenvectors and eigenvalues of this covariance matrix.
- Choose the principal components.
