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