We would like to introduce you to some of the
principles behind face recognition. Currently there are a several methods
to achieve face recognition. Among them we have the neural network
approach, the statistical approach - primarily based on histograms, the
multiresolutional approach, the information theory approach, and the
We would be focusing on the Eigenface approach. This
method was originally suggested by Alex P. Pentland and Matthew A. Turk
from MIT in 1991. This method consist on weighting the difference between
a given face image and a mean image, which is obtained by averaging a
predefined set of faces. The training set is a group of face images from
which the mean face is calculated. Face recognition takes place by
linearly projecting the image to a low dimensional image space and
weighting the difference with respect to a set of eigenvectors. If the
difference (weight) is bellow certain threshold, the image is recognized
as a known face; otherwise, the face can be classified as an unknown face,
or not a face at all.
Some of the limiting factors of this approach are the
background, difference in illumination, imaged head size, and head
orientation. To solve some of these problems we could identify the
location of the head and zoom until we observe most of the face. We could
also set the camera's lighting based on the time of the day.
To learn more about face recognition using eigenfaces visit our
tutorial or some of the following pages.