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Face Recognition Using
Neural Networks
Bhavin Pandya EM2007066
Siddhesh Panderkar EM2006044
Gaurav Hansda EM2006022Hardeepsinh Jadeja EM2006023
Guided By : Prof Hemant Kasturiwale
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What is Face Recognition?
A face recognition system is a computer application for automatically
identifying or verifying a person from a digital image or a video frame
from a video source. One of the ways to do this is by comparing selected
facial features from the image and a facial database.
Feature to be compared for face recognition:
1. Inter-ocular distance
2. distance between the lips and the nose
3. distance between the nose tip and the eyes
4. distance between the lips and the line joining the two eyes
5. eccentricity of the face
6. ratio of the dimensions of the bounding box of the face
7. width of the lips
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What are Neural Network?
A Neural Network is a system of programs and data structures
that approximates the operation of the human brain.
A neural network usually involves a large number of
processors operating in parallel, each with its own small
sphere of knowledge and access to data in its local memory.
Typically, a neural network is initially "trained" or fed large
amounts of data and rules about data relationships (for
example, "A grandfather is older than a person's father").
A program can then tell the network how to behave in
response to an external stimulus or can initiate activity on its
own.
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MODEL OF NEURON
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Neural Network Architecture
Single layer feed forward network.
Multilayer Feedforward Network
Back-Propagation
Self Organizing Map(Unsupervised Learning)
Recurrent Network
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Single layer feedforwardnetwork
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Multilayer FeedforwardNetwork
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Recurrent Networks
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Learning Algorithms
Supervised learning
Unsupervised learning
Reinforcement Learning
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Approaches to Feature Extraction
Appearance Based
Feature Based (Component Based)
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Appearance Based Methods
Principle Component Analysis
Linear Discriminant Analysis
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Block Diagram of DifferentTraining Methods
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PCA based Face Recognition
PCA
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Disadvantages of PCA
Problems with Eigenfaces (PCA)
Different illumination
Different alignment
Different facial expression
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Block Diagram of LDA-NN FaceRecognition System
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Steps For Face Recognition UsingLDA-NN
Assumptions
Square images with W=H=N
M is the number of images in thedatabase
P is the number of persons in thedatabase
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Algorithm For LDA-NN FaceRecognition.
The database
We compute the average of all faces
Compute the average face of each person
And subtract them from the training faces
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We build scatter matrices S1, S2, S3, S4
And the within-class scatter matrixSW
From this scatter matrix we calculate the Fisherface vectors.
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Fisherfaces, the algorithm
The database
2
1
2
N
b
b
b
2
1
2
N
c
c
c
2
1
2
N
d
d
d
2
1
2
N
e
e
e
2
1
2
N
a
a
a
2
1
2
N
f
f
f
2
1
2
N
g
g
g
2
1
2
N
h
h
h
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Fisherfaces, the algorithm
We compute the average of all faces
2 2 2
1 1 1
2 2 21, 8
N N N
a b ha b h
m where M M
a b h
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Fisherfaces, the algorithm
Compute the average face of eachperson
2 2 2 2
2 2 2 2
1 1 1 1
2 2 2 2
1 1 1 1
2 2 2 2
1 1, ,2 2
1 1,
2 2
N N N N
N N N N
a b c d
a b c d x y
a b c d
e f g h
e f g hz w
e f g h
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Fisherfaces, the algorithm
And subtract them from the trainingfaces
2 2 2 2 2 2 2 2
2 2
1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2
1 1 1 1
2 2
, , , ,
,
m m m m
N N N N N N N N
m m
N N
a x b x c y d y
a x b x c y d ya b c d
a x b x c y d y
e z f z
e z fe f
e z
2 2 2 2 2 2
1 1 1 1
2 2 2 2 2 2, ,
m m
N N N N N N
g w h w
z g w h wg h
f z g w h w
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Fisherfaces, the algorithm
We build scatter matrices S1, S
2, S
3,
S4
And the within-class scatter matrixSW
1 2
3 4
, ,
,
m m m m m m m m
m m m m m m m m
S a a b b S c c d d
S e e f f S g g h h
1 2 3 4WS S S S S
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How is Face Recognition using LDA-NN performed
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Advantages of LDA-NN
Faster than Eigen faces
Has lower error rates
Works well even if different illumination Works well even if different facial expressions.
Works well with different allignment.
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Comparison
FERET database
Best Identification rate: eigenfaces(or PCA)80.0%, fisherfaces(or LDA) 93.2%
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Comparison of Different Methodsof Face Recognition
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PROJECT OBJECTIVE
To implement the concept of Neural Networks for the purpose
of Face Recognition.
Further Recognition of unclear images by removing the
background noise. To improve the accuracy of Face recognition by reducing the
number of false rejection and false acceptance errors.
To use Face Thermogram that is output of an infrared camera
to detect the faces in dark environments.
Recognition of images captured while in motion.
Recognition of faces in videos (motion picture).
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Advantages
When an element (Artificial neuron) of the neural networkfails, it can continue without any problem by their parallelnature.
A neural network learns and does not need to bereprogrammed.
It can be implemented in any application.
If there is plenty of data and the problem is poorly understoodto derive an approximate model, then neural networktechnology is a good choice.
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Advantages (contd..)
There is no need to assume an underlying data distributionsuch as usually is done in statistical modeling.
Neural networks are applicable to multivariate non-linear
problems.
The transformations of the variables are automated in thecomputational process.
A neural network can perform tasks that a linear program cannot.
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Applications of FaceRecognition
Passport control at terminals in airports
Participant identification in meetings
System access control Scanning for criminal persons
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Thank you