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FaceRecognitionUsingNeural

NetworksSOUMYA AWASTHI

ROLL NO: 1000118051

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TABLE OF CONTENT

INTRODUCTION

HOW IS FACE RECOGNITION PERFORMED

WHAT ARE NEURAL NETWORKS

MODEL OF NEURON

PROJECT OBJECTIVE

BLOCK DIAGRAM OF FACE RECOGNITION

CURVELET& LDA ALGORITHUM

LDA

RBF NETWORK

CLASSIFIER DESIGN

RBFN BASED FACE RECOGNITION

ANALYSIS

OTHER TECHNIQUE-PCA

WHY FACE RECOGNITION

APPLICATION

LIMITATIONS

CONCLUSION

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INTRODUCTION

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 facialfeatures 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 eyes4. distance between the lips and the

line joining the two eyes

5. eccentricity of the face

6. width of the lips

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How is Face Recognition

Performed?

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Here presents a RBF classifier and LDA based

algorithm gives efficient and robust face

recognition. In general, there are three importantmethods for face recognition such as-

Holistic method, feature-based method and

hybrid method.

The holistic method is used in which the input

is taken as the whole face region and based on

LDA it simplifies a feature set into lower

dimension while retaining the characteristics of

featureset.1. Holistic methods. These methods identify a

face using the whole face image as input. The

main challenge faced by these methods is how to

address the extremely small sample problem.

2. Local methods. These methods use the local

facial features for recognition. Care should be

taken when deciding how to incorporate global

configurational information into local face model.

3. Hybrid methods. These methods use both the

local and holistic features to recognize a face.

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These methods have the potential to offer

better performance than individual holistic orlocal methods, since more comprehensive

information could be utilized.

The security threats and challenges posed in

the current world necessitate the need to have a

reliable and robust method to identify and

authenticate people. Authentication implies

granting people access to a physical area orcomputer system based on the proof they

provide for the identity they claim.

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A person can be authenticatedbased on :

(1) Something the person knows.

For example,

passwords and PIN numbers.

(2) Something the person is.

For example,

Identity cards and security tokens.

(3) Something the person has.

For example,

biometrics.

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Biometrics is being used increasingly now-a-days

as a reliable method of providing valid

authentication over the traditional methods such

as passwords and tokens. Biometric

authentication relies on identifying people based

on certain specific biological characteristics such

as fingerprints, face, iris, retina and a persons

signature that change less frequently over a

period of time. Biometrics can be used along with

other categories of authentication, that is,

something the person knows or has. This termed

as multi-factor authentication.

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Face Recognition Technology (FRT) has a

variety of potential applications in information

security, law enforcement and surveillance, smart

cards, access control, among others [1-3]. For

this reason, FRT has received significantly

increased attention from both the academic and

industrial communities during the past twenty

years. Several authors have recently surveyed

and evaluated the current FRTs from different

aspects. For example, Smal et al. and Valentin

et al. surveyed the feature-based and the

neuralnetwork- based techniques, respectively,

Yang et al. reviewed face detection techniques

[6], Pantic and Rothkrantz [7] surveyed the

automatic facial expression analysis, Daugman

[3] pointed out several critical

issues involved in an effective face

recognition system, while the most recent and

comprehensive survey is possibly from that of

Zhao et al [1], where many of the latest

techniques are reviewed.

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This problem, called the one sample per person

problem (or, one sample problem for short), is

defined as follows. Given a stored database offaces with only one image per person, the goal is

to identify a person from the database later in

time in any different and unpredictable poses,

lighting, etc from the individual image. Due to its

challenge and significance for real-worldapplications, this problem has rapidly emerged

as an active research sub-area of FRT in recent

years, and many ad hoc techniques have been

developed to attack this problem, such as

synthesizing virtual samples, localizing the singletraining image, probabilistic matching and neural

network methods.

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What are Neural Network?

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.

A Neural Network is a system of programs

and data structures that approximates the

operation of the human brain. 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 tobehave in response to an external stimulus or

can initiate activity on its own.

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MODEL OF NEURON

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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 andfalse acceptance errors.

Recognition of images captured while in

motion.

Recognition of faces in videos (motion

picture).

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BLOCK DIAGRAM OF FACE

RECOGNITION SYSTEM

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Brief Review of Curvelets AndLDA Algorithm

Curvelet transform used to extract features from

facial images, and then uses RBFN to classify

facial images basedon features. In a two

dimensional image two adjacent regions can

often have differing pixel values. Such a gray

scale image will have a lot of edges i.e.

discontinuity along a general curve and

consequently curvelet transform will capture this

edge information.

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Linear Discriminant Analysis

LDA is a common statistical technique to find out

the patterns in high dimensional data [8].Feature

extraction, also called dimensionality reduction isdone by LDA for three main reasons they are:

i) To reduce dimension of the data to more

tractable limits.

ii) To capture salient class-specific features of thedata.

iii) To eliminate redundancy.

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RBF NETWORKSTRUCTURE

RBFN contains one input layer and one output

layer with a single hidden layer as shown infigure.

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An RBF neural network structure is similar to a

traditional three-layer feed forward neuralnetwork. The construction of the RBF neural

network involves three different layers with feed

forward architecture. The input layer of this

network is a set of n units, which accept the

elements of an n dimensional input feature

vector. The input units are fully connected to the

hidden layer with r hidden units . Connections

between the input and hidden layers have unit

weights and, as a result, do not have to be

trained. The goal of the hidden layer is to cluster

the data and reduce its dimensionality. In this

structure hidden layer is named RBF units. The

RBF units are also fully connected to the output

layer. The output layer supplies the response of

neural network to the activation pattern applied to

the input layer . The transformation from the input

space to the RBF-unit space is nonlinear,

whereas the transformation from the RBF unit

space to the output space is linear.

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CLASSIFIER DESIGN

Neural networks have been employed andcompared to conventional classifiers for a

number of classification problems. The results

shows that the accuracy of the neural network

approaches equivalent to, or slightly better than,

other methods. It is due to the simplicity,generality andgood learning ability of the neural

networks[4].

RBFN have found to be very attractive for many

engineering problem because:

(1) they are universal approximators,

(2) they have a very compact topology and

(3) their learning speed is very fast because of

their locally tuned neurons.

Therefore the RBF neural networks serve as an

excellent role for pattern applications carried out

to make the learning process, this type of

classification is faster than the other network

models.

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RBFN based FaceRecognition

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ANALYSIS OF THE RESULT

In this features are extracted first and dimension

reduction is carried out from facial images .RBFN

is used to classify the facial images . figure

shows that out of 200 images have been used

most of them recognized by RBFN and the best

average recognition rate is 98.6% .The

performance of recognition rate is better than the

other techniques.

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No of

Images

Acceptance Ratio (%) Execution Time (Seconds) Recognition Rate (%)

LDA Curvelets LDA +

Curvelets

with RBFN

LDA Curvelets LDA +

Curvelets

with RBFN

LDA Cur-

velets

LDA +

Curvelets

with

RBFN

40 92.4 93.1 96.5 38 37 36 87 88 92.1

60 90.6 91.2 94.3 46 44 43 88 90 94.3

120 87.9 88.9 92.8 55 54 50 90 91 95.4

160 85.7 86.9 90.2 67 65 58 91 93 97.7

200 83.5 84.9 87.1 74 70 67 92 95 98.6

The acceptance ratio of LDA is 83.5% compared

with curvelets acceptance ratio is high that is84.9%.The proposed method acceptance ratio is

87.1%. The recognition rate of LDA is 92%

compared with curvelets is high recognition rate

that is 95% represented The proposed method

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recognition rate is 98.6% it shows the figure it is

better than other methods.

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Other technique used forface recognition

We have discussed one technique, one more

technique is Neural and PCA based algorithmfor efficient and robust face recognition This is

based on principal component-analysis (PCA)

technique, which is used to simplify a dataset into

lower dimension while retaining the

characteristics of dataset.

Pre-processing, Principal component analysis

and Back Propagation Neural Algorithm are the

major implementations of this paper. Pre-

processing is done for two purposes-

(i) To reduce noise and possible convolute

effects of interfering system,

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(ii) To transform the image into a different space

where classification may prove easier by

exploitation of certain features.

PCA is a common statistical technique for finding

the patterns in high dimensional datasFeature extraction, also called Dimensionality

Reduction, is done by PCA for a three main

purposes like

i) To reduce dimension of the data to more

tractable limits.

ii) To capture salient class-specific features of

the data,

iii) To eliminate redundancy

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All these processes are implemented for Face

Recognition, based on the basic block diagram

as shown in fig

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PRINCIPAL COMPONENT

ANALYSIS

PCA is a popular technique, to derive a set of

features for both face recognition.

Any particular face can be

(i) Economically represented along the eigen

pictures coordinate space, and

(ii) Approximately reconstructed using a

small collection of Eigen pictures.

To do this, a face image is projected to severalface templates called eigenfaces

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PCA Algorithm:-(i) Acquire an initial set of M face images (the

training set) & Calculate the eigen-faces from the

training set, keeping only M' eigenfaces that

correspond to the highest eigenvalue

(ii) Calculate the corresponding distribution in M'-

dimensional weight space for each known

individual, and calculate a set of weights basedon the input image

(iii) Classify the weight pattern as either a known

person or as unknown, according to its distance

to the closest weight vector of a known person

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Back Propagation Neural Networks

Algorithm:-

Here learning process in Back propagation

requires pairs of input and target vectors. The

output vector o is compared with target vectort.

In case of difference of o andtvectors, the

weights are adjusted to minimize the difference.

Initially random weights and thresholds are

assigned to the network. These weights are

updated every iteration in order to minimize the

mean square error between the output vectorand the target vector.

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Experimentation and Results

One of the images as shown in fig 4a is taken as

the Input image. The mean image and

reconstructed output image by PCA, is as shown

in fig 4b and 4c.

In BPNN, a training set of 50 images is as shown

in fig 5a and the Eigen faces and recognized

output image are as shown in fig 5b and 5c.

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Both of the techniques are being used for facial

recognition. Both play an important role in face

recognition . So , its not easy to say which one is

better of the two.

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WHY FACIALRECOGNITION

Facial recognition offers several advantagesover other biometric identification and

authentication techniques, as facial recognition

system have the following advantages :

(1) Non-intrusive and hence more

convenient.

(2) Hygienic as no physical contact is

involved.

(3) Easy to use as only need to face a

camera.

These advantages make facial recognition

suitable for a wide range of applications across

industry verticals

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Applications of FaceRecognition

Passport control at terminals in airports.

Participant identification in meetings.

System access control.

Scanning for criminal.

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LIMITATIONS

Not so robust against extreme variations in

expression.

Cannot be used for faces with lateral head

rotation.

Face recognition alone as of now, cannot be

successfully used for authentication but can beused for identification and classification.

Identical twins cannot be distinguished using

face recognition

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CONCLUSION

Facial recognition is an upcoming area. There

are a lot of potential scenarios where it can be

used. Considering this, it is not completely

reliable to be used as an independent techniquefor authentication. Facial recognition may be

thrown out of gear with plastic surgery, so it

might need to be combined with other

technologies such as Iris scan to improve

reliability. With improvement in tools and

technologies, it is only a matter of time before the

incidence of false identification of subjects is

reduced to an acceptable level to make facial

recognition more useful.