Biometric Recognition Methodology part 2/4 - Feature Extraction

3.3 Feature Extraction

Previously each face image, $\Gamma_i$ of size   is converted into a big matrix where each row, M presented the image and column is P = XY  and revenue difference matrix A with its size (M x P).  This section (Figure 3.5) described the eigenvalues and eigenvectors using Jacobi’s method, dimension reductions, eigenfaces transformations, features vectors representations and how the eigenfaces is used to rebuild the face images.

 Figure 3.5: Diagram for Eigenfaces Formations

Biometric Recognition Methodology part 1/4 - Intro and Preprocessing

METHODOLOGY


                  
                  
3.1 Introduction

  
    This chapter describes the implementation of the chosen method using the suitable theory. Hence the methodology which described how the difference magnificent mathematical is combined together to achieve the research objectives. There are four (4) phases in the proposed face recognition system namely; Preprocessing, Feature Extraction, Training and Recognition. Each phase is briefly described as follows:


a)    Preprocessing. In this phase, the face dataset acquisition and the preprocessing of the face images are performed.


b)    Feature extraction. Those face library images were prepared for the feature extraction phase. This phase is performed to find the useful feature such as eigenvalues $(\lambda_i )$, eigenvectors$(\ev_i )$ , eigenfaces$(\U_i )$  and feature vectors(\Omega) .


c)    Training phase. Trained feature vectors then used for backpropagation neural network training to generalized the neural network weights for recognition phase.


d)    Recognition phase. The set of chosen eigenfaces, feature vectors and neural network weights is then used for recognition phase. The recognition begins by selecting a face image from face library which the system considered the unknown face.
  
Figure 3.1 illustrates the methodology used to recognize an unknown human face. The figure clearly shows where the four phases is located. For the training and recognition, three (3) models are purposed in the research. Each these phase is then described with their algorithm in this chapter.

Figure 3.1: Proposed modeling System