Eigenfaces
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).
The eigenfaces can now be used to
represent new faces: we can project a
new (mean-subtracted) image on the
eigenfaces and thereby record how that
new face differs from the mean face.
The eigenvalues associated with each
eigenface represent how much the
images in the training set vary from
the mean image in that direction. We
lose information by projecting the
image on a subset of the eigenvectors,
but we minimise this loss by keeping
those eigenfaces with the largest
eigenvalues.
Fisherfaces and Eigenfaces
If your faces aren t aligned, then I d recommend reading the following paper:
Support Vector Machines
Abstract: We present a component-based method
and two global methods for face
recognition and evaluate them with
respect to robustness against pose
changes. In the component system we
first locate facial components,
extract them and combine them into a
single feature vector which is
classified by a Support Vector Machine
(SVM).
The two global systems recognize faces
by classifying a single feature vector
consisting of the gray values of the
whole face image. In the first global
system we trained a single SVM
classifier for each person in the
database. The second system consists
of sets of viewpoint-specific SVM
classifiers and involves clustering
during training.
