Gubbi Compre Report

7/31/2019 Gubbi Compre Report

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Application of Soft Computing to

Face Recognition

Research scholar Abdullah Gubbi

(4PA09PEM03)

Guide Dr. Mohammad Fazle Azeem

Department Of Electronics & Communication Engineering

PA College of Engineering, MANGALORE

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Contents

Introduction.

Literature Survey.

Objective of the work.

Work carried out so far.

Neural Network Based Face Recognition.

Summary of the eigen-face Recognition Procedure.

Type-2 Fuzzy Logic for Edge Detection of Gray Scale Images.

Edges Detection by Type-2 FIS.

Results and Discussions.

Further work to be carried out.

Conclusion

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Introduction

Face recognition is one of the most important abilities

that we use in our daily lives.

Research in automatic face recognition started in the

1960s.

Because of the nature of the problem, not only computer

science researchers are interested in it, but also

neuroscientists and psychologists.

It is widely believed that one can instantly recognise

thousands of people with whom one is familiar. As with

many perceptual abilities, the ease with which humans

can recognise faces disguises the complexity of the task.

Introduction


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Why Face Recognition?

Security Fight terrorism

Find fugitives

Personal information access

ATM Sporting events

Home access (no keys or passwords)

Any other application that would want personal identification

Improved human-machine interaction

Personalized advertising

Beauty search

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Face Recognition System Requirements

Want the system to be inexpensive enough to

use at many locations.

Match almost instantaneously

Before the person walks away from the advertisement

Before the fugitive has a chance to run away

Ability to handle a large database

Ability to do recognition in varying environments

5Introduction


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Problem Statement

Given still or video

images of a scene,

identify one or more

persons in the scene

using a stored

database of faces,

or/and with availablecollateral information

such as race, age

and gender may be

used in narrowingthe search.

Introduction


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What Is Difficult About Face Recognition

Lighting variation

Orientation variation (face angle)

Size variation

Large database Processor intensive

Time requirements

8Introduction

Facial Variations: (a) Original Image (b) noise , (c) expression, (d)

illumination , (e) pose , and (f) ageing


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Variations in pose Head positions,frontal view, profileview and head tilt, facial expressions

Illumination Changes Light direction and intensity changes,cluttered background, low quality images

Camera Parameters Resolution, color balance etc.

Occlusion Glasses, facial hair and makeup

Challenges


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Face Normalization

Adjustment

Expression

Rotation

Lighting

Scale

Head tiltEye location

10Introduction


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General Observations about Fuzzy Logic

Conceptually easy to understand

Tolerant to imprecise data.

Built on top of the experience of experts.

Model nonlinear functions of arbitrary

complexity.

Blended with conventional control techniques.

11Introduction


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ANNs in Real Face Recognition

Many architectures are available but

MLP is popular with back propagation

algorithm.Disadvantages: Complex and difficult to

train

Difficult to implement

Sensitive to lighting variation

12Introduction


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General Image Types

Still image (digital photograph)

Still image can vary a lot from picture to

picture, need face detectionDynamic image (Video camera)

Dynamic image requires motion detection

and head tracking

13Introduction


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Static Matching

Mugshots matching are the

most common application in

this group.

Typically, images in mug

shots applications are of

good quality, consistent with

existing law enforcementstandards

These standards could

involve the type of

background, illumination,

resolution of the camera,and the distance between

the camera and the person

being photographed.

Dynamic Matching

The images available

through a video camera

tend to be of low quality.

segmenting a face in the

crowd difficult.

One may also be able to do

partial reconstruction of theface image using existing

models.

One of the strong

constraints of this

application is the need for

real-time recognition.

14Introduction


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Literature Survey

The objective is to explore approaches,

algorithms, and technologies available for

automated face recognition.

The contemporary face recognition algorithmscan mainly be classified into two categories.

Model-based schemes: This uses shape and othertexture of the face, along with 3D depth information.

Appearance-based schemes: This uses the holistic

texture features.

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Model-Based Schemes

Changes in shape and changes in texture pattern acrossthe face. Both shape and texture can also vary because

of differences between individual and also due to

changes in expression, lighting, viewpoint variations.

There exists a strong concept known as model basedapproaches (statistical models of appearance),

The approach relies on a large and representative

training set of facial images.

A feature-based system, based on elastic bunch graphmatching, was developed by Wiskott et al.[12] .

2D demorphable face model used through which the

face variations are learned [14],

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Elastic Matching

Elastic matching is one of the pattern recognition techniquesin computer science. Elastic matching (EM) is also known

as deformable template, flexible matching, or nonlineartemplate matching.

Elastic matching can be defined as an optimization problem of two-

dimensional warping specifying corresponding pixels between

subjected images.

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Deformable Models

Templates are allowed to translate, rotate and deform to

fit the best representation of the shape present in image

Employ wavelet decomposition of the face image as key

element of matching pursuit filters to find the subtle

differences between faces

Elastic graph approach, based on the discrete wavelet

transform: a set of Gabor wavelets is applied at a set of

hand-selected prominent object points, so that each point is

represented by a set of filter responses, named as a Jet

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Facial Fiducial Points

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Template Matching Methods

Store a template

Predefined: based on edges or regions

Deformable: based on facial contours (e.g.,

Snakes)

Templates are hand-coded (not learned)

Use correlation to locate faces

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Face Template

Use relative pair-wise

ratios of the brightness of

facial regions (14 16

pixels): the eyes areusually darker than the

surrounding face [Sinha 94]

Use average area

intensity values than

absolute pixel values

See also Point Distribution

Model (PDM) [Lanitis et al. 95]

Ration Template [Sinha 94]

average shape

[Lanitis et al. 95]


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Template-Based Methods: Summary

Pros:

Simple

Cons:

Templates needs to be initialized near the face

images

Difficult to enumerate templates for different

poses.

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Appearance-Based Schemes

Principal Component Analysis (PCA) [15]

Linear Discriminant Analysis (LDA) [16] or Fishers LDA

(FLD) or Fisherface method [43].

Independent Component Analysis (ICA) [19].

Locality Preserving Projections (LPP) [20].

Support Vector Machines (SVM) method [8],

The merits of PCA, LDA and Bayesian subspace

approaches are combined in a single framework modelwas presented in [27].

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Literature Survey


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Principal Component Analysis (PCA)

The PCA is an unsupervised learning technique and hence

does not include the label information of the data.

Given the eigenfaces as basis for a face subspace, a face

image is compactly represented by a low dimensional feature

vector and a face can be reconstructed as a linearcombination of the eigenfaces.

The eigenface based method of face recognition, as proposed

by Turk and Pentland uses PCA to identify the image space

axis with the highest variance in facial characteristics.

Much of the discriminatory information required for recognition

is contained within the higher order statistics of the face

images. [Bartlett et al]

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Linear Discriminant Analysis (LDA)

Belhumeur et al have proposed to solve face recognition using Linear

Discriminant Analysis (LDA), also called Fisherfaces or Fisher's Linear

Discriminant (FLD).

LDA is supervised dimensionality reduction method.

LDA to produce a linear projection into a low dimensional subspace, similar

to that used in the eigenface method.

LDA compute an image subspace in which face image variance ismaximised, similar to that used in the eigenface method.

LDA minimize the distance between faces of the same person (within-class

scatter [SW]) and maximize the distance between faces of different person's(between-class scatter [SW])

The within-class scatterSWis defined as where xc is the mean of class c, xis the total mean, Nc is the number of samples of class ci, and C is thenumber of classes.

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Independent Component Analysis (ICA)

ICA is very similar to PCA, but where PCA minimizes

only the second-order dependencies, ICA alsominimizes higher-order dependencies, finding

components that are non-Gaussian.

ICA originates from solving the blind source separation

problem decomposing the input signal x into a linearcombination of independent source signals.

ICA is a method for finding underlying factors or

components from multivariate (multi-dimensional)

statistical data. What distinguishes ICA from othermethods is that it looks for components that are both

statistically independent, and non-Gaussian.

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ICA vs. PCA decomposition of 2D data set.

ICA vs. PCA decomposition of 2D data set.( a )The bases of PCA (orthogonal) and ICA (non orthogonal).(b) Left: the projection of the data onto the top two principal

components (PCA).

(b) Right: the projection onto the top two independent components (ICA).(From Bartlett et al.)

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Locality Preserving Projections (LPP)

LPP also known as Laplacian faces, was proposed

which optimally preserves the neighborhood structure ofthe data set [20].

The LPP is considered as an alternative to the PCA

method.

The main objective of LPP is to preserve the localstructure of the input vector space by explicitly

considering the manifold structure.

Since it preserves the neighborhood information, its

classification performance is much better than othersubspace approaches like PCA and FLD .

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LPP Model Let there be N number of input data points (x1, x2, , xN), which are in r

M.

The first step of this algorithm is to construct the adjacency graph G of Nnodes, such that node i and j are linked if xi and xj are close with respect to

each other in any of the following two conditions.

k-nearest neighbors: Nodes i and j are linked by an edge, if i is among

k-nearest neighbors of j or vice-versa.

neighbors: Nodes i and j are linked by an edge if kxi xjk2 < , where

kk is the usual Euclidean norm.

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Next step is to construct the weight matrix Wt, which is a sparse symmetric

N N matrix with weights Wtij if there is an edge between nodes i and j, and

0 if there is no edge.Two alternative criterion to construct the weight matrix:

Literature Survey


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LPP Model

The objective function of LPP model is to solve the following generalized

eigen value eigen vector problem:

XLXT a = XDXT a Eq(2.5)

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Note: The XDXT matrix is always singular because of high-dimensional nature of the image

space. To alleviate this problem, PCA is used as the preprocessing step to reduce the

dimensionality of the input vector space.

Literature Survey


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LPP

i

T

i xwy :constraint

ii yx Consider

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LPP

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Laplacian Eigen maps versus LPP

Apply the similar idea for computing low-dimensional

representation

Laplacian Eigenmaps does not form explicit

transformation

LPP computes explicit linear transformation

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Support Vector Machine(SVM)

SVMs introduced in COLT-92 by Boser, Guyon & Vapnik. Became rather

popular since.

Theoretically well motivated algorithm: developed from StatisticalLearningTheory (Vapnik & Chervonenkis) since the 60s.

Empirically good performance: successful applications in many fields

(bioinformatics, text, image recognition, . . . ) SVM are supervised learning models with associated

learning algorithms that analyze data and recognize patterns, used

for classification and regression analysis.

The basic SVM takes a set of input data and predicts, for each given input,

which of two possible classes forms the output, making it a non-

probabilistic binary linear classifier.

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Support vector machine(SVM)

Given a set of training examples, each marked as belonging to one

of two categories, an SVM training algorithm builds a model that

assigns new examples into one category or the other.

An SVM model is a representation of the examples as points in

space, mapped so that the examples of the separate categories are

divided by a clear gap that is as wide as possible.

New examples are then mapped into that same space and predicted

to belong to a category based on which side of the gap they fall on.

In addition to performing linear classification, SVMs can efficiently

perform non-linear classification using what is called the kernel trick,

implicitly mapping their inputs into high-dimensional feature spaces.

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Gaussian Functions

Gaussian functions (GF)modulated by sine waves are called Gabor

functions in the field of signal and image processing.[wiki]

GFs modulated by sine waves are called Gabor functions in the field

of signal and image processing.

GFs form a complete but non-orthogonal basis set. Expanding asignal using this basis provides a localized frequency description.

simultaneous localization of spatial and frequency information.

Gabor Wavelet transform could extract both the time (spatial) and

frequency information from a given signal, and the tunable kernel

size allows it to perform multi-resolution analysis.

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Mixture Model

In statistics, a mixture model is a probabilistic model for representing the

presence of sub-populations within an overall population, without requiringthat an observed data-set should identify the sub-population to which an

individual observation belongs.

The problems associated with "mixture distributions" relate to deriving the

properties of the overall population from those of the sub-populations,

"mixture models" are used to make statistical inferences about theproperties of the sub-populations given only observations on the pooled

population, without sub-population-identity information.

Some ways of implementing mixture models involve steps that attribute

postulated sub-population-identities to individual observations (or weights

towards such sub-populations), in which case these can be regarded astypes of unsupervised learning or clustering procedures. However not all

inference procedures involve such steps.

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Curvelet Transform

Curvelet Transform is a new multi-scale

representation, most suitable for objects

with curves.

Developed by Cands and Donoho (1999).

Still not fully matured.

Seems promising.

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Point and Curve Discontinuities

A discontinuity point affects all the Fourier coefficients inthe domain.

Hence the FT doesnt handle points discontinuities well.

Using wavelets, it affects only a limited number of

coefficients. Hence the WT handles point discontinuities well.

Discontinuities across a simple curve affect all the

wavelets coefficients on the curve.

Hence the WT doesnt handle curves discontinuities well.

Curvelets are designed to handle curves using only a

small number of coefficients.

Hence the CvT handles curve discontinuities well.

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Curvelet Transform

The Curvelet Transform includes four

stages:

Sub-band decomposition

Smooth partitioning

Renormalization

Ridgelet analysis

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Gabor Wavelet

A simple model for the responses of

simple cells in the primary visual cortex.

It extracts edge and shape information.

It can represent face image in a very

compact way.

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Gabor Wavelet

The Gabor transform, named afterDennis Gabor, is a special caseof the short-time Fourier transform. It is used to determine

the sinusoidal frequency and phase content of local sections of a

signal as it changes over time. The function to be transformed is first

multiplied by a Gaussian function, which can be regarded as

a window function, and the resulting function is then transformedwith a Fourier transform to derive the time-frequency analysis. The

window function means that the signal near the time being analyzed

will have higher weight. The Gabor transform of a signal x(t) is

defined by this formula:

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Gabor Wavelet (cont)

Advantages:

Fast

Acceptable accuracy

Small training set

Disadvantages:

Affected by complex background

Slightly rotation invariance

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Gabor Wavelet

Gabor wavelet can be used to extract the

information of face.

Matching with the feature extracted by

Gabor wavelet

Advantages and Disadvantages are the

same as that of Face recognition.

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Gabor Wavelet (cont)

Real Part Imaginary Part

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The Face Processing System

PCA

.

.

.

.

.

.

Gabor

Filtering


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The Face Processing System

ICA

.

.

.

.

.

.

Gabor

Filtering


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DiaPCA

DiaPCA is a diagonalization PCA method. This method maintains

the changes of the correlation between rows and columns

of the image during the image reduced-dimension processing.

It can overcome the shortcomings of 2DPCA[8] which only reflects

the changes between the image rows but ignores the varied change

s between the column. It can keeping the features of the imagein a better level while reducing the image dimensions.

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Proposed Work Aims to Address the Following


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Proposed Work Aims to Address the FollowingIssues:

Give an overview of existing face recognition systems and the current state

of research in this field. Identify the problems associated with existing face recognition systems and

possible avenues of research that may help to address these issues.

Improve the effectiveness of existing face recognition algorithms, by

introduction of additional processing steps or adaptation of the method.

Analyze and evaluate a range of face recognition systems applied to two-

dimensional data, in order to identify the advantages and disadvantages

offered by the various approaches.

Determine the most effective method of combining methodologies from the

range of face recognition techniques, in order to achieve a more effective

face recognition system.

Evaluate this final face recognition system and present results in a standardformat that may be compared with other existing face recognition systems.

Identify limitations of the final face recognition system and propose a line of

further research to combat these limitations.

49Objective of the work


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Outline of a typical face recognition system

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Work carried out so farNeural Network Based Face Recognition

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Face region chopped

Resized

Histogram Equalized

(A)Pre-processing steps.

(B)The histogram of an image before (up) and after (down) the histogram

equalization.

Work carried out so far


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(a)The face space and the three projected images on it. Here u1 and u2 are the

eigenfaces.

(b)The projected face from the training database.

a b



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Work carried out so farSummary of the Eigen-face Recognition Procedure

1. Form a face library that consists of the face images of known individuals.2. Choose a training set that includes a number of images (M) for each person

for the people with some variation in expression and in the lighting.

3. Calculate the MxM matrix L, find its eigenvectors and eigenvalues, and

choose the M' eigenvectors with the highest associated eigenvalues.

4. Combine the normalized training set of images according to Eq

5. to produce M' eigenfaces. Store these eigenfaces for later use.

6. For each member in the face library, compute and store a feature vector

according to Eq. T =[w1 w2 wM ]

7. For each new face image to be identified, calculate its feature vector

according to Eq. T =[w1 w2 wM ]

8. Use these feature vectors as network inputs and simulate network with

these inputs

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(A)Sample Faces (B) Normalized Training image set(D) Average face of theSample Faces(C)Mean face (D) Eigenvalues corresponding to eigenfaces

(E) Eigen Values quickly dropping

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A B

D

c

E


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Results

Number of Hidden Neurons

How to determine the number of hidden neurons is always a discussion

topic in neural networks. The standard equation which does not mean the

equation will work for every network. The rule did not work in our case, so

trial and error is still the best way to work out the optimal number of hidden

units. In our experiments, we trained the Neural neural with differentnumbers of hidden units (20 to 45) and recorded their recognition rates.

Recognition rate for the ORL face database 75%

Recognition rate for the YALE face database 83.333%

Number of Training images = 18

Number of people in training phase = 1

Total number of test images = 15

Number of hidden nodes = 36

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Uncertainty knowledge in Image Processing.

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Block Diagram of Type-2 FIS

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NameOriginal

Image

Gradient

Magnitude

Type-1 FIS Type-2 FIS

TajMahal,

India

Baboon

Leena

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Further work to be carried out

The face recognition process consists

of two phases: feature selection and

classification. Feature selection not

only reduces the dimension of the

data, but also makes verification more

accurate.

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face recognition based on Gabor wavelets and Fuzzy

Logic, and aims to build a new classifier so as to improve

the accuracy of recognition.

Using combination of Wavelet and PCA for featureextraction is the preprocessing method using wavelet

transform before process by PCA. As this technique may

give high recognition rate for face images with the low-

variation.

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From literature it is understood that, the same number of curveletcoefficients contain more edge information compared to wavelet

coefficients. Since object recognition is driven by edge information

we can use more efficiently this information for the better

recognition.

To investigate the effect of kernel-based learning algorithms such as

SVM (Support Vector machine), Kernel-PCA, Kernel-FLD (Fisher

Linear Discrminant) on recognition rate Regular linear subspace

methods can be made to extract non-linear features through the

application of kernel trick.

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Combining domains such as Wavelet and/or Gaussian MixtureModel (GMM) and/or PCA and/or Locality Preserving Projections

(LPP)

These approaches include the advantage of both spatial and

frequency components through the application of wavelets. Theseapproaches may speed up the subsequent Expectation-

Maximization learning of the GMM step through the usage of

reduced number of components as wavelet coefficients. Also to find

their performance under noisy conditions.

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Conclusion

Face recognition is active research area as long as weachieve 100% recognition.

There are many algorithms and Techniques exits, all of

them have some have pros and cons.

One should aim for higher recognition rate with minimum

computation time and it should be robust.

Always there is scope for improvement.

Further work never ends.

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