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.