چکیده (انگلیسی):

Face recognition is one of the biometric techniques used for identification of humans. The design of
the face recognition system includes two basic steps. The first step is the extraction of the image’s
features and the second one is the classification of patterns. Feature extracting is a very important step
in face recognition. The recognition rate of the system depends on the meaningful data extracted from
the face image. If the features belong to the different classes and the distance between these classes are
bigger then these features are important for recognition of the images. In this study, the design of face
recognition system using three different feature extraction techniques- Principal Component Analysis
(PCA), Fisher Linear Discriminant Analysis (FLD) and Fast Pixel Based Matching (FPBM) is
presented. The comparative analysis of the simulation results of these methods is presented.