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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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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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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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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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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

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