Face Recognition Result and Discussion Part 1/4

CHAPTER 4

RESULT AND DISCUSSION

4.1 Introduction

This chapter described the results produced based on the methodology explained in Chapter 3. The results are shown and discussions are provided for each experiment. These experiments are divided into three (3) main parts Principal Components Analysis, training and recognition result and experimental result. Prototype model that is designed for this research purpose is also demonstrated in this chapter. 




4.2 Principal Component Analysis


Sample face images from ORL face dataset is shown in Figure 4.1 respectively. The sample showed seven different persons with different conditions. For easy explanations only three face images from each class or persons is taken as training set. Thus, 21 face images is used as a training set and 49 face images as testing set. The training set is then converted into a big matrix,  with its size  where m is the number of training set and P is equal to number of pixels of each face image.

 Figure 4.1: Example ORL dataset

 

Biometric Recognition Methodology part 4/4 - Normalization

3.6 Normalization


Since the value of Backpropagation neural network required input range from zero (0) to one (1) as used the sigmoid activation function and it is found that most of the result produced in feature extraction using eigenfaces are not in particular range, thus the normalization process are required. In these proposed neural network, three types of different normalizations techniques had been selected for this reason [Puteh Saad, 2001]. They are the simple unit range (SUR), improve unit range (IUR) and improved linear scaling (ILS). Equation (3.42), (3.43) and (3.44) shows the computation needed for SUR, IUR and ILS respectively. The best technique adopted is based on the highest classification rate produced by backpropagation neural network.

With reference to the above equation  refers to the new value of feature,  in each dimension after the normalization process. Furthermore xmax and xmin refer to the maximum and minimum features value respectively. For the ILS computation,  refers to the mean of the feature and  is used for the standard deviation of the features in the same dimension. The dimensions for each vector are defined as .

Biometric Recognition Methodology part 3/4 - Artificial Neural Network Implementations

3.4 Artificial Neural Network Implementations


The capability of neural network to differentiate patterns make the backpropagation neural network is chosen to classify unknown face images. In the thesis, the proposed system implemented the binary sigmoid function in training phase. The binary sigmoid has a normalized range within 0 to 1 which can be described as;

    (3.28)

where c controls the firing angle of the sigmoid.

 Figure 3.8: The sigmoid activation function with different values of c

From figure 3.8 when c is large, the sigmoid becomes like a threshold function and when c is small, the sigmoid becomes more like a straight line (linear). If value of c is large, the learning faster but a lot of information is lost. However, more information is gain although the speed very slow with small amount of c. Because of this function is differentiable, it enables the backpropagation algorithm to adapt the lower layer of weights in a multilayer neural network [Marzuki Khalid, 2005]. This backpropagation algorithm is explained in the following paragraph.

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

LITERATURE REVIEW PART 3/3 - Artificial Neural Network

2.4 Artificial Neural Network


Artificial Neural Network (ANNs) has a large appeal to many AI researchers. A neural network can be defined as model of reasoning based on the human brain. The brain consists of a closely interconnected set of nerve cells or basic information-preprocessing units, called neurons. The human brain incorporates nearly 10 billion neurons and 60 trillion connections, synapses between them [Shepherd, 1990]. By using multiple neurons simultaneously, the brain can perform its functions much faster than the fastest computers in existence today [Negnevitsky, 2002].

2.4.1 Architecture


A multilayer perceptron is a feed-forward neural network with one or more hidden layers. Typically, the network consists of an input layer of source neurons that at least one hidden layer of neurons and an output layer of neurons (Figure 2.3). The input signals are propagated in a forward direction on a layer-by-layer basis. The backpropagation algorithm perhaps is the most popular and widely used neural paradigm. It based on the generalized delta rule proposed by research group in 1985 headed by Dave Rumelhart based at Stanford University, California, USA.

Figure 2.3: Feed-forward Neural Network

LITERATURE REVIEW PART 2/3 - Principal Component Analysis Approach

2.3 Principal Component Analysis Approach


Principal Component Analysis (PCA) is also known as Karhunen-Loeve transformation or eigenspace projection. It is a well known statistical technique to identify patterns in data. It highlight similarities and differences between patterns. Since patterns can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool to extract patterns. The other main advantage of PCA is that once the pattern is found in the data and the data is compressed where the number of dimensions is reduced, however less information is lost [Smith, 2002].

LITERATURE REVIEW PART 1/3 - Biometric Recognition of Human Face Background

CHAPTER 2




LITERATURE REVIEW

2.1 Introduction  


    The human biometric recognition of human face is a popular research topic in computer vision. Its motivation arises in commercial security system. Despite the fact that other biometric recognition identification methods such as fingerprints and iris scans may more accurate, biometric recognition of human face has always been a major research focus because it is noninvasive and it is natural and intuitive to users.


As the biometric recognition of human face is an application in computer vision, hence the standard methodology of the biometric recognition shown in Figure 2.1. In the preprocessing phase, the unwanted noise or irrelevant data is eliminated from the image. Others preprocessing steps include spatial quantization (reducing the number of bits per pixel) or finding regions of interest. The second stage involves transforming the image data into another domain to extract the significant features. Lastly, the extracts features are examined and evaluated

Figure 2.1: Standard Image Analysis Model

INTRODUCTION OF FACE BIOMETRIC RECOGNITION

Machine biometric recognition of human faces is a challenging problem due to the changes in the face identity and variation between images of the same face due to illumination and viewing direction. The issues are how the features are adopted to represent a face under environmental changes and how the classification is done to a new face image based on the chosen representations.


Principal Component Analysis (PCA) which also known as eigenfaces is used in this research to extract set of feature extraction of the faces. The reason that it is chosen due to its capability to extract the relevant information from high dimensional matrix [Turk, 1991]. As for the classification task, the euclidean distance and backpropagation neural network is chosen since most researchers show superior performance claimed that both methods.


A set of features from a face image is representation using the eigenvalues and eigenvectors. In order to obtain eigenvalues and eigenvectors, Jacobi’s method is used due its accuracy and robustness. As for the classification task, backpropagation algorithm is applied.