20120140504019 by iaemedu

VIEWS: 0 PAGES: 11

									 International Journal of JOURNAL OF in Engineering RESEARCH IN ENGINEERING
 INTERNATIONAL Advanced ResearchADVANCEDand Technology (IJARET), ISSN 0976 –
 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME
                               AND TECHNOLOGY (IJARET)

ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
                                                                              IJARET
Volume 5, Issue 4, April (2014), pp. 179-189
© IAEME: www.iaeme.com/ijaret.asp
Journal Impact Factor (2014): 7.8273 (Calculated by GISI)                     ©IAEME
www.jifactor.com




          EFFICIENT FACE RECOGNITION SYSTEM USING HYBRID
                           METHODOLOGY

                                         Keyur Shah1, Vijay Ukani2
            1, 2
                   (Computer Science and Technology, Nirma University, Ahmedabad, India)




 ABSTRACT

         Recognizing frontal countenance of human beings by a computer system is an interesting
 and challenging problem. Facial recognition System has emerged as an adorable solution to address
 many instant needs for identification and the verification of identity claims. It brings together the
 portend of other biometric systems, which attempt to tie identity to individually distinctive features
 of the body. Facial feature extraction consists in restraining the most characteristic face countenance
 such as eyes, nose, and mouth regions within the face images that portray the human faces. In this
 paper, the two most well-known algorithms i.e. PCA and LBP are introduced and the combination
 of Local Binary Pattern (LBP) and Principal Component Analysis (PCA) is presented as our
 proposed approach in which the proposed approach has achieved 93.5% of gain in processing
 memory. LBP algorithm is used as feature extractor of the face image. LBP is used for their
 resistance against changing frontal facial expressions. PCA algorithm is used for dimension
 reduction of the countenance vector. The complete approach has been tested on databases of people
 under different facial expressions.

 Keywords: Face           Recognition,    Local   Binary   Pattern,   Principal   Component   Analysis,
 Hybrid Method.


 I. INTRODUCTION

         Face recognition is one of the most pertinent applications of image analysis. Face detection
 is consists of pre-processing step for face recognition, and as an issue by itself, because it presents
 its own difficulties and challenges, sometimes quite different from face recognition. It is a challenge
 to build an automated system which commensurate human ability to recognize faces.


                                                    179
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME

1.1 Research area of face recognition
        There are wide ranges of research area for face recognition system, which are focused and
implemented by many well-known industries i.e. in automobiles, IT industries, etc. Some known
areas are Information Security, Access management, Biometrics, Law enforcement, Personal
security, Entertainment industry.

1.2 Motivation
        The interest for the efficient face recognition algorithm i.e. recognizing faces which is an
emerging area of research in applications development, i.e. Recognizing people for various
purposes like access control, biometric access, personal security, etc. In such systems the input is
taken as an image from the digital devices and after processing the input image the output is in form
of relevant personal information about the person.

1.3 Scope of paper
        Goal of this paper is to present the work on Hybrid approach, by implementing the efficient
Face Recognition algorithm which can reduce the use of processing memory. This face recognition
system can be used in real world scenario. It can be applied in small scale organizations like
Industries, Universities/Colleges, and Hospitals. Implementing face recognition algorithm that can
be used with as much ease as possible for recognizing faces.




                          Fig 1: Basic model of Face Recognition System

                                                180
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME

II. FACE RECOGNITION DESIGN POINTS OF VIEW

        The most axiomatic face countenances were used in the dawn of face recognition. It was an
intelligent approach to resemble human face recognition ability. There was an effort to try to
measure the importance of certain spontaneous features like eyes, cheeks, mouth and geometric
measures like eye distance, length ratio, etc. Nowadays it is still a pertinent issue, mostly because
eliminating certain facial countenances or features from a face can lead to a better performance [1].
In other words, it is imperative to decide which facial features play an important role to a good
recognition and which features are not vital. However, the influx of abstract mathematical tools like
Eigen faces created another approach to face recognition. It is possible to gauge the similarities
between faces precluding those human-relevant countenances. This new point of view empowered
the new abstraction level, leaving the anthropocentric approach behind. There are still some human-
relevant features that are being taken into account [2]. For example, skin color is an important
countenance for face detection. The region of certain features like mouth and eyes is also used to
perform normalization prior to the feature extraction step. To sum up, a designer can apply to the
algorithms the knowledge that psychology, neurology or simple observation provide.

2.1 Face recognition methodologies
        The work done in face recognition was based on the spatial relationships between facial
landmarks as a means to capture and extract facial features. This method is obviously highly
dependent on the detection of these landmarks which is difficult in variations illumination, shadows
as well as the stability of these relationships across pose variation. These problems were and still
remain significant faltering blocks for face detection and recognition [1]. This work was followed
by a different approach in which the face was treated as a general pattern with the application of
more general pattern recognition approaches, which are based on photometric characteristics of the
image. To implement these approaches a huge variety of algorithms have been developed. Here we
will focus on two of the most powerful streams of work: Principal Components Analysis (PCA) and
Local Binary Pattern (LBP).

2.2 Principal Component Analysis
        One of the most used and cited statistical method is the Principal Component Analysis
(PCA) [4] [5] [6]. It is a mathematical procedure that performs a dimensionality reduction by
extracting the principal components of the multi-dimensional data. The first principal component is
the linear combination of the original dimensions that has the highest variability. The n-th principal
component is the linear combination with the maximum variability, being orthogonal to the n-1 first
principal components. Usually the mean x is extracted from the data. So, let xn, xm be the data
matrix where x1,..., xm are the image vectors (vector columns) and n is the number of pixels per
image.
         T
C x= Λ                                                                                    (1)

Where cx is the covariance matrix of the data.

C x=                                                                                      (2)

Φ=[ 1,……., n] is the eigenvector matrix of cx. Λ is a diagonal matrix, the eigenvalues λ1,…… λn
n of cx are located on its main diagonal. λi is the variance of the data projected on i


                                                 181
                                              Engineering
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME

2.3 Local Binary Pattern
        The original LBP operator, introduced by Ojala et al [7], is a powerful means of texture
                                                                              3x3
description. The operator labels the pixels of an image by thresholding the 3x3- neighbourhood of
each pixel with the centre value and considering the result as a binary number. Then the histogram
           ls
of the labels can be used as a texture descriptor. See Figure 2 for an illustration of the basic LBP
operator. Later the operator was extended to use neighbourhoods of different sizes [8]. Using
                                                                    allow
circular neighbourhoods and bilinear interpolating the pixel values allow any radius and number of
pixels in the neighbourhood. For neighbourhoods we will use the notation (P, R) which means P
sampling points on a circle of radius of R. See Figure 3 for an example of the circular (8, 2)
neighbourhood. Another extension to the original operator uses so called uniform patterns [8]. A
Local Binary Pattern is called uniform if it contains at most two bitwise transitions from 0 to 1 or
vice versa when the binary string is considered circular. For example, 00000000, 00011110 and
     0011
10000011 are uniform patterns. Ojala et al. Noticed that in their experiments with texture images,
uniform patterns account for a bit less than 90% of all patterns when using the (8,1) neighbourhood
and for around 70% in the (16,2) neighbourhood.




                                    2
                                Fig 2: The basic LBP operator [7]




Fig 3: The circular (8,2) neighbourhood. The pixel values are bi-linearly interpolated whenever the
                                                                 bi linearly
                           sampling point is not in the centre of a pixel [7]

        We use notation for the LBP operator LBPu2p,r The subscript represents using the operator
                                                                                    us
in a (P, R) neighbourhood. Superscript u2 stands for using only uniform patterns and labelling all
remaining patterns with a single label. A histogram of the labelled image fl(x, y) can be defined as
                                                182
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME


Hi=        I {fl(x, y) = i} , i = 0, . . . , n − 1,                                           (3)

in which n is the number of different labels produced by the LBP operator and

I {A} =          1, A is true
0, A is false.

        This histogram contains information about the distribution of the local micro-patterns, such
as edges, spots and flat areas, over the whole image. For efficient face representation, one should
retain also spatial information. For this purpose, the image is divided into regions R0,R1, . . . Rm-1
and the spatially enhanced histogram is defined as

Hij=       I {fl(x, y) = i} I {(x, y)    Rj}, i = 0, . . , n−1, j = 0, . . . , m−1            (4)

        In this histogram, we effectively have a description of the face on three different levels of
locality: the labels for the histogram contain information about the patterns on a pixel-level, the
labels are summed over a small region to produce information on a regional level and the regional
histograms are concatenated to build a global description of the face.

2.4 Hybrid Face Recognition System
        LBP is suitable for feature vector needed for fast processing. In the past ten years, the
operator has been widely used in texture classification, image retrieval and other areas such as facial
image analysis. Because of the direct and simple calculation, insensitivity to the light and rotation,
capability for capturing image detail, the operator can extract the patterns of local region which are
more favorable. The image can be considered as a sample of a stochastic process, if the image
elements are of random variables type [8]. The PCA basis vectors are defined as the eigenvectors of
the scatter matrix. PCA technique allows the system to represent the necessary information for
comparing the faces using the little information once the mathematical representation accomplished
which it is need to have a lot of faces to be store. PCA is useful in linear regression in several ways
Identification and elimination of multi-collinearities in the data. PCA projects the data along the
directions where the data varies the most. The eigenvectors calculated from the covariance matrix
corresponds to the largest Eigen values. The magnitude of the Eigen values corresponds to the
variance of the data along the eigenvector directions [9].

           TABLE 1: Comparison table based on various parameters [5][6][7][8][9]
       PARAMETERS              PCA                  LBP            HYBRID METHOD
        Binary Patterns                        No                    Yes               Yes
        Computational                          No                    Yes               Yes
           Simplicity
        Time Required                         Less                Moderate           Very Less
        Effect of Facial                      High                 Less                Less
          Expressions
       Different Lighting                  Moderate                  Less              Less
           Conditions
            Effect of                         High                   Less              Less
          Orientation

                                                        183
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME

III. PROPOSED METHOD

        Combination of Local Binary Pattern and Principal Component Analysis for the face
recognition. LBP helps to recognize face image with small orientation, illumination variances and
expression. PCA will reduce the length of the feature vector. LBP operator works with 8 neighbours
of pixel, using value of centre pixel as a threshold. All neighbours that have values higher than the
value of central pixel will be given value 1 and all those that have lower or equal to value of central
pixel will be given value 0.The eight binary numbers associated with 8 neighbours are then read
sequentially in the clockwise direction to form a binary number. This binary number or its
equivalent in decimal system may be assigned to central pixel. The LBP feature vector, in its
simplest form, Divide the examined window to cells (e.g. 33×28 pixels for each cell).
        For each pixel in a cell, compare the pixel to each of its 8 neighbours. Where the centre
pixel's value is greater than the neighbour, write "1". Otherwise, write "0". This will give an 8-digit
binary number (which is usually converted to decimal for convenience). This binary number will be
considered in clockwise direction. Compute the histogram, over the cell, of the frequency of each
"number" occurring (i.e., each combination of which pixels are smaller and which are greater than
the centre). Optionally normalize the histogram. Concatenate normalized histograms of all cells.
This will give the feature vector for the window. Local Binary Pattern has been applied to
normalize images under varying illuminations and expression. PCA has been considered as a
simple, efficient linear subspace method, many nonlinear techniques such as kernel PCA can be
used. Certain nonlinear methods with certain classifiers do yield better performances consistently
than others. The following works can be carried out in future to improve the face recognition. In
this approach we used Training dataset consists of 760 images of dimension 180×200 of 152
different faces with 5 variations in expressions. Test dataset which is used as input consists of 304
images of dimension 180×200 of 152 different faces with 2 variations in expressions. Facial
features are extracted from the LBP face image and then image is divided into 10 regions LBP
histograms are generated for each window region. The generated vector values is inputted to PCA
for dimension reduction. The input test image will be checked with set of train images After
matching the test image, the results are shown in Ranking order, i.e. first best match will be shown
first.

IV. IMPLEMENTATION

4.1 Local Binary Pattern
        By dividing the examined window into cells (e.g. 16×16 pixels for each cell). For each pixel
in a cell, compare the pixel to each of its 8 neighbours (on its left-top, left-middle, left-bottom,
right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise. Where the
centre pixel's value is greater than the neighbour’s value, write "1". Otherwise, write "0". This gives
an 8-digit binary number (which is usually converted to decimal for convenience). Compute the
histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of
which pixels are smaller and which are greater than the centre). Optionally normalize the histogram.
Concatenate (normalized) histograms of all cells. This gives the feature vector for the window. The
algorithm for LBP is as, where I is number of images, neigh is neighbouring cell, WHT is the
weight of neighbouring pixels to generate the histogram Histo.




                                                 184
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME


                            input : I,WHT
                            output: Histo
                            INIT Histo[] to 0;
                            INIT t[] to 0;
                            foreach pixel in I do
                            foreach element k in neigh do
                            if neigh[k] is greater than pixel then
                            SET t[k] to 1;
                            End
                            End
                            SET LBPCode to sumof(WHT*t);
                            ADD 1 to hist[LBPCode] ;
                            End
                                         Algorithm for LBP

4.2 Principal Component Analysis
        Dimension Reduction Technique is the first step of PCA. In this we will create a matrix of
no. of Images arranged in Columns(n) and the no. of pixels of image in arranged in Row(m) as an
input I. After this in second step we will calculate the mean, finding covariance matrix i.e.
C=A*A(T). Center portion of image is calculated by subtracting the covariance from column (pixel
of original image). Eigen value is equals to no. of image × no. of pixels. It will create matrix of
[E,V] Eigen matrix. Eigen Faces is equal to Centered * Vectors. We have to calculate the ratio of
centered value by vector. The largest value of the ratio will be selected and the Eigen face matrix is
calculated. The algorithm for PCA is as, where I is number of images, N is the output, STR is the
string which stores the converted image number as string, M is for calculating mean value.


                         input : I
                         output : N
                         foreach image-no in train-number do
                         STR = Convert integer-to-string(image-no);
                         STR = Concatenate (Str, image-type);
                         STR = Concatenate (train-database-path, Str);
                         I = image-read(STR);
                         I = Convert( rgb-to-gray(I));
                         [image-no-row, image-no-col] = size(image);
                         temp = Reshape(image, image-row*image-col);
                         T = [T temp]; end
                         M = MEAN(I) A = A-M
                         C = TRANSPOSE(A)*A
                         [U,S,V]=Eigen(C)
                         Ureduce =U(:,1:K);
                         Z=TRANSPOSE(Ureduce)*TRANSPOSE(I);
                         N=TRANSPOSE(Z);
                         End
                                       Algorithm for PCA



                                                 185
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME

4.3 Hybrid Method
        Selecting dataset to Train and Test the images. Applying LBP to get the frontal facial feature
and extracting facial countenance then applying PCA to extracted features this will generate
reduced dimension feature vector of the images. Comparing the test input image to the trained
dataset and the result is shown in ranked order. The algorithm for Hybrid method is as, where I is
number of images, WHT is the weight of neighboring pixels to generate the histogram Histo, STR is
the string which stores the converted image number as string, M is for calculating mean value. Here
the input to the PCA is the generated histogram Histo.

                     input : I,WHT
                     output : Histo
                     INIT Histo[] to 0;
                     INIT t[] to 0;
                     foreach pixel in I do
                     foreach element k in neigh do
                     if neigh[k] is greater than pixel then
                     SET t[k] to 1;
                     end
                     end
                     SET LBPCode to sumof(WHT*t);
                     ADD 1 to histo[LBPCode] ;
                     I=Histo
                     M = MEAN(I)
                     A = A-M
                     C = TRANSPOSE(A)*A
                     [U,S,V]=Eigen(C)
                     Ureduce =U(:,1:K);
                     Z=TRANSPOSE(Ureduce)*TRANSPOSE(I);
                     N=TRANSPOSE(Z);
                     MIN=999;
                     foreach i=1 to no-of-images do
                     Dist(i)=N(i)-Query(i)
                     if Dist(i) less than MIN then
                     MIN=Dist(i) POS=i
                     end
                     end
                     End


Algorithm for Hybrid Method
        This work implemented the proposed Hybrid approach in Matlab Version 7.12.0.635
(R2011a) 64-bit (win64), for image database, we used ESSEX database which consists of 152
individual images of person [9] female (20), male (132) with little variations in frontal face
expressions. In this we have selected 304 images as input of 152 individual images with 2 variations
each to test against trained database of 760 images of 152 individual images with 5 variations each,
and after processing the result is shown in ranked order i.e. first best match will show at first
position as:



                                                 186
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME




                    Fig 4: Output of result in ranking order using hybrid method


V. RESULT ANALYSIS

        Principal Component analysis (PCA) is a worthy method for finding patterns in data with
ability to express it in a way that similarities and differences are focused. As the dimensionality of
data increases finding patterns in data become more difficult, PCA is a great tool for this purpose.
Local Binary Pattern (LBP) is a simple and very efficient texture operator. It creates the binary
pattern of every pixel of an image. The most important property of LBP operator in real-world
applications is its robustness to monotonic gray scale changes. It is also computationally simple. In
PCA Eigen faces, we need rows × columns i.e. if image sizes 256 × 256 then 65535 pixels have to
be stored. In LBP an image is represented by a feature vector of length 768. PCA require 1572840
bytes of processing memory for single image, LBP requires 116736 bytes of processing memory for
single image. In hybrid approach the output of LBP i.e. 768 values is compressed using PCA to 50
values. So using hybrid approach an image can be represented using a feature vector of length 50
and the result is also not compromised. Using hybrid approach by implementing first LBP in our
algorithm we need 116736 bytes of processing memory, after applying the PCA to this input we
now need only 60800 bytes, 93.5% gain in processing memory is achieved.

                  TABLE 2: Required processing memory by different algorithms
                           Algorithm             Processing Memory
                             PCA                    1572840 Bytes
                              LBP                    116736 Bytes
                      HYBRID METHOD                  60800 Bytes




                                                 187
                                              Engineering
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME




       Fig 5: Comparison of processing memory required by LBP, PCA, and Hybrid method


VI. CONCLUSION

        This work has presented the different algorithms, the proposed approach and various
algorithms with their efficiency. The algorithms PCA, LBP and Hybrid approach are studied and
implemented the results were analysed and from that we can conclude that though the LBP requires
less processing memory, and if we have large number of image database the required processing
       ry
memory as compare to PCA would be less. The hybrid approach will make some good difference in
terms of reduction to processing memory (i.e. 93.5% gains) as compare to these existing algorithms.
                                                              constrained
Face recognition systems used today work very well under constrained conditions, although all
systems work much better with frontal images and constant lighting.

VII. REFERENCES

 [1]   Ion Marques Face Recognition Algorithms, Proyecto Fin de Carrera June 2010.
 [2]   Study of Different Algorithms
       http://ethesis.nitrkl.ac.in/1701/2/B.pdf.
 [3]   W. Zhao, R. Chellappa, A. Rosenfeld, and P. Phillips Face recognition: A literature survey
                                           399
       ACM Computing Surveys, pages 399-458 2003
 [4]                                                                 loeve
       M. Kirby and L. Sirovich Application of the karhunen-loeve procedure fo the        for
       characterization of human faces IEEE Transactions on Pattern Analysis and Machine
                                  108,
       Intelligence, 12(1):103-108, 1990
 [5]   M. Turk and A. Pentland Eigenfaces for recognition Journal of Cognitive Neuroscience,
       3(1):71-86, 1991.
 [6]                             irby Low dimensional
       L. Sirovich and M. Kirby Low-dimensional procedure for the characterization of human
                                                         A
       faces Journal of the Optical Society of America A- Optics, Image Science and Vision,
       4(3):519-524, March 1987.


                                               188
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME

 [7]    Timo Ahonen, Abdenour Hadid, and Matti Pietikainen Face Recognition with Local Binary
        Patterns Machine Vision Group, Infotech Oulu,FIN-90014 University of Oulu, Finland.
 [8]    L.I. Smith A tutorial on Principal Component Analysis Cornell University, USA, 2002.
 [9]    Etemad, K., Chellappa Discriminant analysis for recognition of human face images Journal
        of the Optical Society of America 14 1997.
 [10]   Computer Vision Science Research Projects
        http://cswww.essex.ac.uk/mv/allfaces/faces94.html
 [11]   Abhishek Choubey and Girish D. Bonde, “Face Recognition Across Pose With Estimation
        of Pose Parameters”, International Journal of Electronics and Communication Engineering
        &Technology (IJECET), Volume 3, Issue 1, 2012, pp. 311 - 316, ISSN Print: 0976- 6464,
        ISSN Online: 0976 –6472.
 [12]   S. K. Hese and M. R. Banwaskar, “Appearance Based Face Recognition by PCA and LDA”,
        International journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2,
        2013, pp. 48 - 57, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
 [13]   Sambhunath Biswas and Amrita Biswas, “Fourier Mellin Transform Based Face
        Recognition”, International Journal of Computer Engineering & Technology (IJCET),
        Volume 4, Issue 1, 2013, pp. 8 - 15, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
 [14]   Prof. B.S Patil and Prof. A.R Yardi, “Real Time Face Recognition System using Eigen
        Faces”, International Journal of Electronics and Communication Engineering & Technology
        (IJECET), Volume 4, Issue 2, 2013, pp. 72 - 79, ISSN Print: 0976- 6464, ISSN Online:
        0976 –6472.
 [15]   U.K.Jaliya and J.M.Rathod, “A Survey on Human Face Recognition Invariant to
        Illumination”, International journal of Computer Engineering & Technology (IJCET),
        Volume 4, Issue 2, 2013, pp. 517 - 525, ISSN Print: 0976 – 6367, ISSN Online:
        0976 – 6375.
 [16]   J. V. Gorabal and Manjaiah D. H., “Texture Analysis for Face Recognition”, International
        Journal of Graphics and Multimedia (IJGM), Volume 4, Issue 2, 2013, pp. 20 - 30,
        ISSN Print: 0976 – 6448, ISSN Online: 0976 –6456.




                                              189

								
To top