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.