Face Recognition Using Face Recognition Using Neural NetworksNeural Networks

Bhavin Pandya EM2007066Bhavin Pandya EM2007066Siddhesh Panderkar EM2006044Siddhesh Panderkar EM2006044Gaurav Hansda EM2006022Gaurav Hansda EM2006022Hardeepsinh Jadeja EM2006023Hardeepsinh Jadeja EM2006023

Guided By : Prof Hemant KasturiwaleGuided By : Prof Hemant Kasturiwale

What is Face Recognition?What is Face Recognition? A A face recognition systemface recognition system is a computer application for automatically is a computer application for automatically

identifying or verifying a person from a digital image or a video frame 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 from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database.facial features from the image and a facial database.

Feature to be compared for face recognition:Feature to be compared for face recognition:1.1. Inter-ocular distanceInter-ocular distance

2.2. distance between the lips and the nosedistance between the lips and the nose

3.3. distance between the nose tip and the eyesdistance between the nose tip and the eyes

4.4. distance between the lips and the line joining the two eyesdistance between the lips and the line joining the two eyes

5.5. eccentricity of the faceeccentricity of the face

6.6. ratio of the dimensions of the bounding box of the faceratio of the dimensions of the bounding box of the face

7.7. width of the lipswidth of the lips

What are Neural Network?What are Neural Network?

A Neural Network is a system of programs and data structures A Neural Network is a system of programs and data structures that approximates the operation of the human brain.that approximates the operation of the human brain.

A neural network usually involves a large number of A neural network usually involves a large number of processors operating in parallel, each with its own small processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. sphere of knowledge and access to data in its local memory.

Typically, a neural network is initially "trained" or fed large Typically, a neural network is initially "trained" or fed large amounts of data and rules about data relationships (for amounts of data and rules about data relationships (for example, "A grandfather is older than a person's father"). example, "A grandfather is older than a person's father").

A program can then tell the network how to behave in A program can then tell the network how to behave in response to an external stimulus or can initiate activity on its response to an external stimulus or can initiate activity on its own.own.

MODEL OF NEURONMODEL OF NEURON

Neural Network ArchitectuNeural Network Architecturere

Single layer feed forward network.Single layer feed forward network.

Multilayer Feedforward NetworkMultilayer Feedforward Network Back-PropagationBack-Propagation Self Organizing Map(Unsupervised Learning)Self Organizing Map(Unsupervised Learning)

Recurrent Network Recurrent Network

Single layer feedforward Single layer feedforward networknetwork

Multilayer Feedforward Multilayer Feedforward NetworkNetwork

Recurrent NetworksRecurrent Networks

Learning AlgorithmsLearning Algorithms

Supervised learning

Unsupervised learning

Reinforcement Learning

Approaches to Feature Extraction

Appearance Based Feature Based (Component Based)

Appearance Based Methods

Principle Component Analysis Linear Discriminant Analysis

Block Diagram of Different Block Diagram of Different Training MethodsTraining Methods

PCA based Face RecognitionPCA based Face Recognition

PCA

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Disadvantages of PCADisadvantages of PCA

Problems with Eigenfaces (PCA)Problems with Eigenfaces (PCA)• Different illuminationDifferent illumination• Different alignmentDifferent alignment• Different facial expressionDifferent facial expression

Block Diagram of LDA-NN Face Recognition System

Steps For Face Recognition Using LDA-NN

Assumptions• Square images with W=H=N • M is the number of images in the

database• P is the number of persons in the

database

Algorithm For LDA-NN Face Recognition.

The database

We compute the average of all faces

Compute the average face of each person

And subtract them from the training faces

We build scatter matrices We build scatter matrices S1S1, , S2S2, , S3S3, , S4S4

And the within-class scatter matrix And the within-class scatter matrix SWSW

From this scatter matrix we calculate the Fisher From this scatter matrix we calculate the Fisher face vectors.face vectors.

Fisherfaces, the algorithm

The database

2

1

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f

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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 h

a b hm where M

M

a b h

Fisherfaces, the algorithm

Compute the average face of each person

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 dx y

a b c d

e f g h

e f g hz w

e f g h

Fisherfaces, the algorithm

And subtract them from the training faces

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

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e z f z

e z fe f

e z

2 2 2 2 2 2

1 1 1 1

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N N N N N N

g w h w

z g w h wg h

f z g w h w

Fisherfaces, the algorithm

We build scatter matrices S1, S2, S3, S4

And the within-class scatter matrix SW

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

How is Face Recognition using LDA-NN performed

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Advantages of LDA-NNAdvantages of LDA-NN

Faster than Eigen facesFaster than Eigen faces Has lower error ratesHas lower error rates Works well even if different illuminationWorks well even if different illumination Works well even if different facial expressions.Works well even if different facial expressions. Works well with different allignment.Works well with different allignment.

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ComparisonComparison FERET databaseFERET database

Best Identification rate: eigenfaces(or PCA) Best Identification rate: eigenfaces(or PCA) 80.0%, fisherfaces(or LDA) 93.2%80.0%, fisherfaces(or LDA) 93.2%

Comparison of Different Methods Comparison of Different Methods of Face Recognitionof Face Recognition

PROJECT OBJECTIVEPROJECT 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).

AdvantagesAdvantages When an element (Artificial neuron) of the neural network When an element (Artificial neuron) of the neural network

fails, it can continue without any problem by their parallel fails, it can continue without any problem by their parallel nature.nature.

A neural network learns and does not need to be A neural network learns and does not need to be reprogrammed.reprogrammed.

It can be implemented in any application.It can be implemented in any application.

If there is plenty of data and the problem is poorly understood If there is plenty of data and the problem is poorly understood to derive an approximate model, then neural network to derive an approximate model, then neural network technology is a good choice.technology is a good choice.

Advantages (contd..) Advantages (contd..)

There is no need to assume an underlying data distribution There is no need to assume an underlying data distribution such as usually is done in statistical modeling. such as usually is done in statistical modeling.

Neural networks are applicable to multivariate non-linear Neural networks are applicable to multivariate non-linear problems.problems.

The transformations of the variables are automated in the The transformations of the variables are automated in the computational process.computational process.

A neural network can perform tasks that a linear program can A neural network can perform tasks that a linear program can not.not.

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Applications of Face Applications of Face RecognitionRecognition

• Passport control at terminals in airportsPassport control at terminals in airports• Participant identification in meetingsParticipant identification in meetings• System access controlSystem access control• Scanning for criminal personsScanning for criminal persons

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