Face recognition has been an active research area over the last 30
years. The face is our primary focus of attention in social intercourse,
playing a major role in conveying identity and emotion. Although the
ability to infer intelligence or character from facial appearance is
suspect, the human ability to recognize faces is remarkable. We can
recognize thousands of faces learned throughout our lifetime and
identify familiar faces at a glance even after years of separation. This
skill is quite robust, despite large changes in the visual stimulus due
to viewing conditions, expression, aging, and distractions such as
glasses or changes in hair style. In this book, Laplacian faces which
uses linear projective projection is studied and finally enhanced before
accuracy. LPP is designed for preserving local structure; it is likely
that a nearest neighbour search in the low dimensional space will yield
similar results to that in the high dimensional space. LPP's are linear
projective maps that arise by solving a variational problem that
optimally preserves the neighborhood structure of the data set. Finally
the algorithm is modified to yield better results in terms of time and
accuracy.