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
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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
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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.
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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
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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
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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.
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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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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:
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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.
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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.
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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.
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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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Further work to be carried out
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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Further work to be carried out
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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Further work to be carried out
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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References
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