Efficient Face Recognition Using Hybrid PCA-LBP

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
179
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.
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ISSN 0976 - 6480 (Print)
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Volume 5, Issue 4, April (2014), pp. 179-189
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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

181
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.
Cx=ϕΛϕT
(1)
Where cx is the covariance matrix of the data.
Cx=
ଵ
௠
∑ ‫ݔ‬௜‫ݔ‬௜
்௠
௜ୀ଴ (2)
Φ=[ϕ1,……., ϕn] 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 ϕi

International Journal of Advanced Research in Engine
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp.
2.3 Local Binary Pattern
The original LBP operator, introduced by
description. The operator labels the pixels of an image by thresholding the 3x3
each pixel with the centre value and considering the result as a binary number. Then the histogram
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
circular neighbourhoods and bilinear interpolating the pixel values allo
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,
neighbourhood. Another extension to the
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
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.
Fig 2
Fig 3: The circular (8,2) neighbourhood. The pixel values are bi
sampling point is not in the centre of a pixel [7]
We use notation for the LBP operator
in a (P, R) neighbourhood. Superscript
remaining patterns with a single label. A histogram of the labelled image
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976
6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME
182
The original LBP operator, introduced by Ojala et al [7], is a powerful means of texture
description. The operator labels the pixels of an image by thresholding the 3x3- neighbourhood of
ls can be used as a texture descriptor. See Figure 2 for an illustration of the basic LBP
circular neighbourhoods and bilinear interpolating the pixel values allow any radius and number of
sampling points on a circle of radius of R. See Figure 3 for an example of the circular (8,
neighbourhood. Another extension to the original operator uses so called uniform patterns [8]. A
and for around 70% in the (16,2) neighbourhood.
Fig 2: The basic LBP operator [7]
The circular (8,2) neighbourhood. The pixel values are bi-linearly interpolated whenever the
sampling point is not in the centre of a pixel [7]
We use notation for the LBP operator LBPu
2p,r The subscript represents 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
ering and Technology (IJARET), ISSN 0976 –
© IAEME
Ojala et al [7], is a powerful means of texture
neighbourhood of
ls can be used as a texture descriptor. See Figure 2 for an illustration of the basic LBP
w any radius and number of
sampling points on a circle of radius of R. See Figure 3 for an example of the circular (8, 2)
original operator uses so called uniform patterns [8]. A
linearly interpolated whenever the
The subscript represents using the operator
stands for using only uniform patterns and labelling all
can be defined as

183
Hi=∑௫,௬ 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} = ቄ
1
0
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=∑௫,௬ 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
Simplicity
No Yes Yes
Time Required Less Moderate Very Less
Effect of Facial
Expressions
High Less Less
Different Lighting
Conditions
Moderate Less Less
Effect of
Orientation
High Less Less

184
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.

185
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

186
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:

187
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

International Journal of Advanced Research in Engine
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp.
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
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.
Face recognition systems used today work very well under constrain
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
ACM Computing Surveys, pages 399
[4] M. Kirby and L. Sirovich Application of the
characterization of human faces IEEE Transactions on Pattern Analysis
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] L. Sirovich and M. Kirby Low
faces Journal of the Optical Society of America A
4(3):519-524, March 1987.
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976
6499(Online) Volume 5, Issue 4, April (2014), pp. 179-189 © IAEME
188
Comparison of processing memory required by LBP, PCA, and Hybrid method
The algorithms PCA, LBP and Hybrid approach are studied and
ry as compare to PCA would be less. The hybrid approach will make some good difference in
Face recognition systems used today work very well under constrained conditions, although all
systems work much better with frontal images and constant lighting.
Ion Marques Face Recognition Algorithms, Proyecto Fin de Carrera June 2010.
Study of Different Algorithms
http://ethesis.nitrkl.ac.in/1701/2/B.pdf.
W. Zhao, R. Chellappa, A. Rosenfeld, and P. Phillips Face recognition: A literature survey
ACM Computing Surveys, pages 399-458 2003
M. Kirby and L. Sirovich Application of the karhunen-loeve procedure fo
human faces IEEE Transactions on Pattern Analysis
108, 1990
M. Turk and A. Pentland Eigenfaces for recognition Journal of Cognitive Neuroscience,
irby Low-dimensional procedure for the characterization of human
faces Journal of the Optical Society of America A- Optics, Image Science and Vision,
ering and Technology (IJARET), ISSN 0976 –
© IAEME
Comparison of processing memory required by LBP, PCA, and Hybrid method
The algorithms PCA, LBP and Hybrid approach are studied and
ry as compare to PCA would be less. The hybrid approach will make some good difference in
ed conditions, although all
Ion Marques Face Recognition Algorithms, Proyecto Fin de Carrera June 2010.
W. Zhao, R. Chellappa, A. Rosenfeld, and P. Phillips Face recognition: A literature survey
loeve procedure for the
human faces IEEE Transactions on Pattern Analysis and Machine
M. Turk and A. Pentland Eigenfaces for recognition Journal of Cognitive Neuroscience,
dimensional procedure for the characterization of human
Optics, Image Science and Vision,

189
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