Face recognition using DCT and PCA approach

Abstract

A complex multidimensional structure like face needs good computing techniques for
recognition. In this thesis face recognition is done by Principal Component Analysis (PCA) and
by Discrete Cosine Transform (DCT). Face images are projected onto a face space that encodes
best variation among known face images. The face space is defined by eigenface which are
eigenvectors of the set of faces. In the DCT approach we transform the image into the frequency
domain and extract the feature from it. For feature extraction we use two approach.. In the 1st
approach we take the DCT of the whole image and extract the feature from it. In the 2nd
approach we divide the image into sub-images and take DCT of each of them and then extract
the feature vector from them.