The Using Of PCA, Wavelet and GLCMIn Face Recognition System, A Comparative Study

Abstract

The process of data dimension reduction plays an important role in any face recognition system because many of these data are repetitive and irrelevant and this cause a problem in applications of data miningandlearning the machine. The main purpose is to improve the performance of recognition by eliminating repetitive features.In this research, a number of data reduction techniques were used like: Principal Component Analysis,Gray-Level Co-occurrence Matrix andDiscrete Wavelet Transformfor extracting the most important features from the images of persons. A different number of training and testing images were used to compare the performance of each of the techniques above in the recognition process. Euclidean distance scale was used to get results. 1-IntroductionThe distinction of biometrics refers to the automatic discrimination of individuals based on the vectors of the traits derived from their physiological or behavioral characteristics. The most commonly used biometric methods are face recognition[1]. Face recognition has become one of the most challenging tasks of distinguishing patterns in past decades. Face recognition applications include: surveillance, forensics and forensic applications, secure electronic banking, smart cards, to identify the individual in international transport centers, to control access and in many other areas[2][3].The face is one of the oldest techniques used in people's recognition, it can be classified in to[3][4]:Structural method:- It relies on some face features like nose, eyes, eyebrows, mouth, chin, and relative relationships.Holistic method:- depends on the overall shape of the face (Global Shape of the Face).Hybrid method:-it relies on both holistic and Structural methods. It is generally used for 3D face recognition.