face features are extracted from fuzzy and LBP method tested on SVM and KNN

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A NOVEL LBP FUZZY FEATURE
EXTRACTION METHOD FOR
FACE RECOGNITION
By
ABDULLAH GUBBI
PA College Of
Engineering
Karnataka
MADASU HANMANDLU
Senior IEEE Member,
EE Dept., IIT Delhi,
New Dehi, India
MOHAMMAD FAZLE AZEEM
Senior IEEE Member,
EE Dept.AMU
UP

2. Agenda
2
 Face recognition.
 Fuzzy logic.
 Local Binary Pattern.
 Information set.
 K-Nearest Neighbour classifier; Support Vector Machine.
 Results and Conclusion.

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Face recognition
 Face recognition has received much attention during the past few decades.
 Challenges (I) pose variation (front, non-front), (ii) occlusion, (iii) image
orientation, (iv) illumination condition and (v) facial expression.
 Algorithms (I)Structure-based schemes that make use of shape and other
texture of the face along with 3D depth information.(II) Appearance-based
schemes that make use of the holistic texture features.
 The Eigen faces (PCA) and Fisher faces (LDA) methods are based on the holistic
approach .

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PCA & LDA
 The Eigen faces (PCA) and Fisher faces (LDA) methods are based on
the holistic approach for face recognition.
 Eigen-faces approach is likely to find the wrong components on
images when there is a large variation in illumination, since the data
points with the maximum variance over all classes are not necessarily
useful for classification.
 The Fisher-faces are computationally expensive.
 Looking at the shortcoming of the above techniques and taking
advantage of Information sets, which have been developed by
Hanmandlu to enlarge the scope of fuzzy sets.

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Experiment using Haar 10 wavelet de-noising[4]
Recognition
rate
PCA
LDA
ORL database
66.07% 86.07%
(with noise)
Proposed
database
66.43% 89.29%
(without noise)
KPCA
FA
49.29%
85.70%
51.07%
86.07%
Where, PCA- Principle component analysis,
LDA-Linear Discriminant Analysis,
KPCA-Kernel Principle component analysis
and
FA- Fisher Analysis.

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Local Binary Patterns
 The Local Binary Pattern (LBP) method is widely used in 2D texture
analysis. The LBP operator is a non-parametric 3x3 kernel which
describes the local spatial structure of an image.
 Introduced by Ojala et al.
 LBP is defined as an ordered set of binary comparisons of pixel
intensities between the centre pixel and its eight surrounding pixels.
The decimal values of the resulting 8-bit word (LBP code) leads to
28 possible combinations, which are called Local Binary Patterns

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Fuzzy Logic
 Fuzzy Logic (FL) theory is the extension of conventional (crisp) set theory.
 It was introduced by Zadeh .
 Deals with the fuzzy sets having imprecise and uncertain data. It handles the
concept of partial truth (truth values between completely true and
completely false.
 Used to model the vagueness and ambiguity in complex systems for which
there is no mathematical model to describe.
 The drawback of fuzzy sets is that they treat the property or attribute values
which we say information source values and their membership function
values separately for all the problems dealing with the fuzzy logic theory.
 consider a set of students graded based on the performance of the class
topper as the benchmark and the student’s individual performance
(information source value) is determined by comparing his performance with
that of the topper (µ).

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Proposed method
 Instead of considering the whole image and their gray values, divide the
image into sub images of size 3x3 (enclosed in the window)non
overlapping .In case of LBP it is overlapping.
 Extract local information using LBP method
 We compute a membership function for each window based on the
centre pixel of the window using:
ic
window
i( x, y )
i( x, y )
 Where i c represent the centre pixel in the window and
represents the sum of the gray values in the same window.
 The information at the central pixel is given by the product of the
membership value and central pixel value as per the concept of
information value.
Hc
window
ic

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Proposed method
conti….
 This information is modified by a scale factor = F max with the result the
scaled information is
HS
F max H c
where λ is a scaling parameter which is greater than one (λ>1). and
Fmax
0 . 50
In order to take account of the information from the neighbourhood pixels
(information sources), we compute the LBP value for the window.
Lw
LBP ( x c , y c )
By taking a clue from the communication theory, the information about the
neighbourhood pixels is taken to be the log of the decimal value:
HN
log L w

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Proposed method
conti….
 Having the information at the central pixel and the neighbourhood
pixels, the total information is taken as the product of these two types
of information, given by
H
HSHN
 The contribution of this method is that it eliminates the shortcoming of
LBP approach that ignores the central pixel value by accounting for
the information of both the central and the neighbourhood pixels

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Support Vector Machine (SVM)
 A classifier derived from statistical learning theory by Vapnik, et al. in
1992
 SVM became famous when, using images as input, it gave accuracy
comparable to neural-network with hand-designed features in a
handwriting recognition task
 Currently, SVM is widely used in object detection &
recognition, content-based image retrieval, text
recognition, biometrics, speech recognition, etc.
 Also used for regression.
V. Vapnik

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KNN Classifier
 The k-Nearest Neighbour classifier is amongst the simplest of all the
machine learning methods.
 It is a non-parametric method for classifying objects. Nonparametric in the sense that one need not worry about the
underlying structure.
 Classification is done based on how much close the test feature
vector is to the training feature vectors in the feature space.
 An object is classified based on the majority votes of its neighbours.
If k = 1, then the object is simply assigned to the class of its nearest
neighbour.

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some of the subjects in databases
 Fig 2. Near-infrared face images of some of the subjects in the CSIST
database.
Fig 3. Gray scale face images of some of the subjects in ORL database
.

14. Experimental Results
14
The Recognition rates obtained on ORL database with SVM and KNN classifiers
ORL Database
Poly1
Poly 2
KNN
Training images 7
Testing images 3
Training images 3
Testing images 7
Training images 5
Testing images 5
Training images 4
Testing images 6
96.66%
96.66%
92.5%
82.55%
80.35%
82.55%
87.5%
87.5%
88%
88.33%
87.916%
87.08%
Recognition rate for CSIST near infra red database with SVM and KNN classifier.
CSIST Near Infra Red
Database
Training images 1
Testing images 3
Training images 2
Testing images 2
Poly1
Poly 2
KNN
89.33%
87.66%
89.33%
92%
92%
91.55%

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Flow chart of
implementation
Start
For number of images
Input image train or test
tr Normalize image
Divide into 3x3 windows
For each window
Compute µ, Compute sum
Pick centre pixel and
Compute information set or feature
Store features in database
Stop

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
Conclusion
A novel approach is presented to account for the information from both the central pixel
and the neighbourhood pixels of a face image while matching test sample with the training
samples in the face recognition process .
 The information value is defined as the product of information source value and its
membership function value.

A comparison of performance of the proposed approach is made with that of PCA using
two classifiers KNN and SVM. Better results are reported with SVM.
 The proposed approach is found to be effective on images having variation in
expression, illumination and pose. Further work needs to be done by changing the
membership function values. This is possible one way by employing type-2 membership
functions in which one of the parameters is changed.

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References
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 [4]Isra’a Abdul-Ameer Wavelet Based Image De-noising to Enhance the Face Recognition Rate,
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 [6]M. Hanmandlu, Information sets and Information Processing, A Research Report, IIT Delhi,
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