Face Recognition Using PCA and DCT Based Approach

Abstract

Face is a complex multidimensional structure and needs good computing techniques for recognition. Our approach treats face recognition as a two-dimensional recognition problem. 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 take 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.