الفهرس 
CHAPTER 1: Biometrics: An OverviewOnly 14 pages are availabe for public view
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Abstract
Authentication is the process of determining whether someone or something is, in fact, who or what it is declared to be. In general,
authentication methods can be broadly categorized into three groups
1) knowledge-based, which typically relies on a password. 2) object-based,
which relies on a physical possession such as tokens or smart cards.
3) Identity-based, i.e. biometrics, which relies on the uniqueness of
Physiological or behavioral characteristics of a person. The term biometrics
has been used to denote the unique biological traits of individuals, such as
face images, fingerprints, iris, voice print, etc., that can be used for
identification. Since these traits cannot be stolen, lost, or forgotten, they
offer better inherent security and reliability in identifying people. Recently,
there is a considerable effort to replace traditional means of identification
such as the use of passwords with biometric-based authentication systems.
The general architecture of a traditional biometric authentication
system consists of two main phases: training (enrollment) and testing
(recognition). The biometric sensor captures the data from the biometric
and sends biometric data to the feature extraction module that extracts
selected information (features) from the data and creates a unique feature
vector for the biometric sample. In the enrollment phase, these features are
stored in a database as templates. In the recognition phase, the matcher
receives the extracted feature vector and compares it with the feature vector
of all templates which are already stored in the database (for identification)
or with one specific template (for verification).
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Biometrics authentication systems are gaining wide-spread
popularity in recent years due to the advances in sensor technologies as
well as improvements in the matching algorithms that make the systems
both secure and cost-effective. A biometric system can operate in
verification and identification modes. In computer science, in particular,
biometrics is used as a form of identity access management and access
control. It is also used to identify individuals in groups that are under
surveillance.
Face recognition is the one of the most important topic in biometrics
authentication and it has drawn attention of the research community. In
addition, it has become one of the most active applications of visual pattern
recognition due to its potential value for law enforcement, surveillance, and
human-computer interaction. Nowadays, various systems are able to
properly recognize people based on their face image. The recognition of a
face in a single image involves at least these three stages: Face detection,
Feature extraction, and Face recognition. Face recognition systems solves,
mainly, two kinds of problems [21]: identification problems (answer the
question: “Who am I?”), where the input to the system is unknown face,
and the system gives back the determined identity from a database of
known individuals; and verification problems (answer the question: “Am I
who I say I am?”), which verify that the individual is who he claims to be.
Several surveys papers [12, 22, 23, 24, 25, 26] and books [27, 28, 29] on
human and machine recognition of faces have been published, which gave
very good reviews on face recognition. In order to find out the true
invariant for face recognition, researchers have developed recognition
algorithms such as Principal Component Analysis (PCA, also known as
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Eigenfaces) [18, 20], Fisher Discriminant Analysis (FDA, also known as
Fisherfaces, Linear Discriminant Analysis) [15], Self Organizing Map and
Convolutional Network (SOM+CN) [30], template matching [31], Modular PCA [32], Line Edge Maps (LEM) [16], Elastic Bunch Graph Matching
(EBGM) [33], Directional Corner Point (DCP) [34], Local Binary Patterns
(LBP) [35], and etc. Face recognition is a challenging task because of
variable factors like alterations in scale, location, pose, facial expression,
occlusion, lighting conditions and overall appearance of the face. With the
synergy of efforts from researchers in diverse fields different frameworks
have evolved for solving the problem of face recognition.
In this work, we develop some techniques to answer the question
”Who am I? ”. For this purpose, we propose a face recognition technique
based on edge detection and distance similarity measures. Accordingly,
mean square error and correlation coefficient are used as a distance
similarity measures for matching. A second technique for face recognition
based on genetic programming is proposed. An advantage of the proposed
techniques is that they are not affected by face recognition aspects such as
lighting condition, varying facial expression, and varying pose. In addition,
the results demonstrate that the proposed techniques can obtain better
performances than other existing face recognition techniques.
The thesis comprises five chapters, these are organized as follows Chapter one: gives an overview of biometrics, biometrics types, and their
applications.
Chapter two: surveys face recognition algorithms and face recognition
challenges.
Chapter three: reviews edge detection and proposes face recognition
technique based on edge detection and distance similarity
measures. Also, comparisons between this proposed
technique and other existing techniques are introduced.
Chapter four: reviews genetic programming and proposes a face
recognition technique based on genetic programming.
Also, comparisons between this proposed technique and
other existing techniques are considered.
Chapter five: draws the conclusion and suggests headlines for future work.