facerecexplanation.m

fisherface based image retrival

%FISHERFACES FOR FACE RECOGNITION
%
% We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression.
% Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take
% advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear
% subspace of the high dimensional image space梚f the face is a Lambertian surface without shadowing. However, since faces are
% not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than
% explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the
% face with large deviation. Our projection method is based on Fisher抯 Linear Discriminant and produces well separated classes in a
% low-dimensional subspace, even under severe variation in lighting and facial expressions. The Eigenface technique, another method
% based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive
% experimental results demonstrate that the proposed 揊isherface