19-02-2013, 02:50 PM
FACE RECOGNITION USING NEURAL NETWORK
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ABSTRACT
Facial recognition systems are computer-based security systems that are able to
automatically detect and identify human faces. Facial recognition has gained
increasing interest in the recent decade. Over the years there have been several
techniques being developed to achieve high success rate of accuracy in the
identification and verification of individuals for authentication in security systems.
This project experiments the concept of neural network for facial recognition that can
differentiate and recognize face of image. This face recognition system begins with
image pre-processing and then the output image is trained using Backpropagation
algorithm. Backpropagation network learns by training the inputs, calculating the error
between the real output and target output, and propagates back the error to the network
to modify the weights until the desired output is obtained. After training the network,
the recognition system is tested to ensure that the system can recognize the pattern of
each face image. The purpose of this project is to recognize face of image for the
recognition analysis using Neural Network. This project is mainly concern with offline
facial recognition systems using purely image processing technique. The system will
find database image has a maximum percentage on similarity of the pattern of the
image. This project is also to design a pattern recognition system by applying Neural
Network Toolbox in MATLAB software.
INTRODUCTION
PROJECT BACKGROUND
Face recognition is very important for our daily life. It can be used for remote
identification services for security in areas such as banking, transportation, law
enforcement, and electric industries, etc. For this security access project is aimed at
demonstrating facial recognition techniques that could antiquate, substitute, or
otherwise, supplement, conventional key, and can be used as an alternative to existing
fingerprint biometrics method. A computerized system equipped with a digital camera
can identify the face of a person and determine if the person is authorized to start the
vehicle. This integrated system would be able to authorize a user before switching on
the vehicle with a key. Whilst facial recognition systems are by now readily available in
the market, the vast majority of them are installed at large open spaces, such as in
airport halls. The focus of this project is, thus, to compare the extracted feature with
face image database for the recognition analysis using Neural Network.
PROBLEM STATEMENT
Face recognition is a pattern recognition task performed specifically on faces. It
can be described as classifying a face either "known" or "unknown", after comparing it
with stored known individuals. It is also desirable to have a system that has the ability
of learning to recognize unknown faces. Computational models of face recognition
must address several difficult problems. This difficulty arises from the fact that faces
must be represented in a way that best utilizes the available face information to
distinguish a particular face from all other faces. Faces pose a particularly difficult
problem in this respect because all faces are similar to one another in that they contain
the same set of features such as eyes, nose, and mouth arranged in roughly the same
manner.
In view of the high rate of crime, fraud and terrorism in the world today, it is
becoming increasingly more important to have remote monitoring systems that work
well with other security devices. As security threats and frauds become increasingly
rampant, it is necessary to have systems that allows recognition and monitoring of
unauthorized people being in an area, using an equipment.
VERIFICATION VS. IDENTIFICATION
In general, face recognition consists of two different ways of usage, which are face
verification and face identification. The former is used for verification of the owner and
the latter is used for matching input identity with registered identity in the database. For
mobile device applications, since these mobile devices are mainly used for personal use,
the face verification is mainly used rather than the identification. So from now on, we
will describe face verification mainly. There are two different ways to recognize a
person: verification and identification. Verification (answers the question “Am I who I
claim I am?”) involves confirming or denying a person's claimed identity. In
identification, the system has to recognize a person (addressing the question “Who am
I?”) from a list of N users in the template database. Identification is a more challenging
problem because it involves 1:N matching compared to 1:1 matching for verification.
THESIS ORGANIZATION
This thesis composed of 5 chapters each will detail out of details upon every
aspect of this project. The beginning of this thesis will explain on what foundation the
system to be built on. Chapter 1 has introduction, to give definition of the project
background to the facial recognition system and how we could use it in security
propose. The problem statement was also mentioned in this chapter as well. Project
objectives and scopes of the methodology of this project had been included, at the end
of chapter 1 the organization of this thesis had been come.
BASIS OF FACIAL RECOGNITION PROCESS – THE PCA
The recognition process involves comparing the Eigenface weights for two faces
using a proprietary algorithm that generates a match score. Different faces will produce
a poor match score; images of the same face will produce a good match score.
The process begins by reducing the variability of the human face to a set of
numbers. Using a mathematical technique called Principal Components Analysis
(PCA), one can examine a large group of faces and extract the most efficient building
blocks required to describe them. It turns out that any human face can be represented as
the weighted sum of 128 of these building blocks, known as Eigenfaces, based on the
pioneering works of M. Turk and A. Pentland [5]. With this technique, the essence of a
human face can be reduced to just 256 bytes of information. The recognition process
involves comparing the Eigenface weights for two faces using a proprietary algorithm
that generates a match score. Different faces will produce a poor match score; images of
the same face will produce a good match score.
The system requires a one-to-one comparison, the Eigenface weights of
authorized personnel are recorded in a central database. When someone appears before
the camera, his or her face is quickly compared to all of the faces in the database to see
if it generates a match.
Model of neuron
A neuron is an information-processing unit that is fundamental to the operation of a
neural network. In this case of artificial neural networks, the strength of the connection
between an input and a neuron is defined as the value of the weight. Negative weight
values correspond to inhibitory connections, while positive values correspond to
excitatory connections. The adder sums up all the inputs modified by their respective
weights. Finally, a transfer function controls the amplitude of the output of the neuron.
An acceptable range of output is usually between 0 and 1, or -1 and 1 depending on the
transfer function selected. Figure 2.1 shows a typical model of an artificial neuron.
[attachment=51354]
ABSTRACT
Facial recognition systems are computer-based security systems that are able to
automatically detect and identify human faces. Facial recognition has gained
increasing interest in the recent decade. Over the years there have been several
techniques being developed to achieve high success rate of accuracy in the
identification and verification of individuals for authentication in security systems.
This project experiments the concept of neural network for facial recognition that can
differentiate and recognize face of image. This face recognition system begins with
image pre-processing and then the output image is trained using Backpropagation
algorithm. Backpropagation network learns by training the inputs, calculating the error
between the real output and target output, and propagates back the error to the network
to modify the weights until the desired output is obtained. After training the network,
the recognition system is tested to ensure that the system can recognize the pattern of
each face image. The purpose of this project is to recognize face of image for the
recognition analysis using Neural Network. This project is mainly concern with offline
facial recognition systems using purely image processing technique. The system will
find database image has a maximum percentage on similarity of the pattern of the
image. This project is also to design a pattern recognition system by applying Neural
Network Toolbox in MATLAB software.
INTRODUCTION
PROJECT BACKGROUND
Face recognition is very important for our daily life. It can be used for remote
identification services for security in areas such as banking, transportation, law
enforcement, and electric industries, etc. For this security access project is aimed at
demonstrating facial recognition techniques that could antiquate, substitute, or
otherwise, supplement, conventional key, and can be used as an alternative to existing
fingerprint biometrics method. A computerized system equipped with a digital camera
can identify the face of a person and determine if the person is authorized to start the
vehicle. This integrated system would be able to authorize a user before switching on
the vehicle with a key. Whilst facial recognition systems are by now readily available in
the market, the vast majority of them are installed at large open spaces, such as in
airport halls. The focus of this project is, thus, to compare the extracted feature with
face image database for the recognition analysis using Neural Network.
PROBLEM STATEMENT
Face recognition is a pattern recognition task performed specifically on faces. It
can be described as classifying a face either "known" or "unknown", after comparing it
with stored known individuals. It is also desirable to have a system that has the ability
of learning to recognize unknown faces. Computational models of face recognition
must address several difficult problems. This difficulty arises from the fact that faces
must be represented in a way that best utilizes the available face information to
distinguish a particular face from all other faces. Faces pose a particularly difficult
problem in this respect because all faces are similar to one another in that they contain
the same set of features such as eyes, nose, and mouth arranged in roughly the same
manner.
In view of the high rate of crime, fraud and terrorism in the world today, it is
becoming increasingly more important to have remote monitoring systems that work
well with other security devices. As security threats and frauds become increasingly
rampant, it is necessary to have systems that allows recognition and monitoring of
unauthorized people being in an area, using an equipment.
VERIFICATION VS. IDENTIFICATION
In general, face recognition consists of two different ways of usage, which are face
verification and face identification. The former is used for verification of the owner and
the latter is used for matching input identity with registered identity in the database. For
mobile device applications, since these mobile devices are mainly used for personal use,
the face verification is mainly used rather than the identification. So from now on, we
will describe face verification mainly. There are two different ways to recognize a
person: verification and identification. Verification (answers the question “Am I who I
claim I am?”) involves confirming or denying a person's claimed identity. In
identification, the system has to recognize a person (addressing the question “Who am
I?”) from a list of N users in the template database. Identification is a more challenging
problem because it involves 1:N matching compared to 1:1 matching for verification.
THESIS ORGANIZATION
This thesis composed of 5 chapters each will detail out of details upon every
aspect of this project. The beginning of this thesis will explain on what foundation the
system to be built on. Chapter 1 has introduction, to give definition of the project
background to the facial recognition system and how we could use it in security
propose. The problem statement was also mentioned in this chapter as well. Project
objectives and scopes of the methodology of this project had been included, at the end
of chapter 1 the organization of this thesis had been come.
BASIS OF FACIAL RECOGNITION PROCESS – THE PCA
The recognition process involves comparing the Eigenface weights for two faces
using a proprietary algorithm that generates a match score. Different faces will produce
a poor match score; images of the same face will produce a good match score.
The process begins by reducing the variability of the human face to a set of
numbers. Using a mathematical technique called Principal Components Analysis
(PCA), one can examine a large group of faces and extract the most efficient building
blocks required to describe them. It turns out that any human face can be represented as
the weighted sum of 128 of these building blocks, known as Eigenfaces, based on the
pioneering works of M. Turk and A. Pentland [5]. With this technique, the essence of a
human face can be reduced to just 256 bytes of information. The recognition process
involves comparing the Eigenface weights for two faces using a proprietary algorithm
that generates a match score. Different faces will produce a poor match score; images of
the same face will produce a good match score.
The system requires a one-to-one comparison, the Eigenface weights of
authorized personnel are recorded in a central database. When someone appears before
the camera, his or her face is quickly compared to all of the faces in the database to see
if it generates a match.
Model of neuron
A neuron is an information-processing unit that is fundamental to the operation of a
neural network. In this case of artificial neural networks, the strength of the connection
between an input and a neuron is defined as the value of the weight. Negative weight
values correspond to inhibitory connections, while positive values correspond to
excitatory connections. The adder sums up all the inputs modified by their respective
weights. Finally, a transfer function controls the amplitude of the output of the neuron.
An acceptable range of output is usually between 0 and 1, or -1 and 1 depending on the
transfer function selected. Figure 2.1 shows a typical model of an artificial neuron.