  Face Recognition is most used for biometric user authentication that
identifies a user based on his or her facial features. The system is in high
demand, as it is used by many businesses and employed in many devices such as
smartphones and surveillance cameras. However, one frequent problem that is
still observed in this user-verification method is its accuracy rate. Numerous
approaches and algorithms have been experimented to improve the stated flaw of
the system. This research develops one such algorithm that utilizes a
combination of two different approaches. Using the concepts from Linear Algebra
and computational geometry, the research examines the integration of Principal
Component Analysis with Delaunay Triangulation; the method triangulates a set
of face landmark points and obtains eigenfaces of the provided images. It
compares the algorithm with traditional PCA and discusses the inclusion of
different face landmark points to deliver an effective recognition rate.
