Feature selection using modified particle swarm optimisation for face recognition

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 02 | Feb-2015, Available @ http://www.ijret.org 679
FEATURE SELECTION USING MODIFIED PARTICLE SWARM
OPTIMISATION FOR FACE RECOGNITION
Hemalatha Gayatri L1
, Govindan V.K2
1
PG scholar, Department of Computer science & engineering, National Institute of Technology Calicut, Kerala, India.
2
Professor, Department of Computer science & engineering, National Institute of Technology Calicut, Kerala, India.
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all
features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the
computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as
features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the
choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and
giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of
97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50
iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face
recognition, classification rate.
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1. INTRODUCTION
Humans have the ability to recognize faces easily and
effortlessly but in the area of image analysis and computer
vision it remained as a difficult problem on which many
years of research is going on. A complete review of
techniques for face recognition can be found in [1]. An
unknown digital image is identified from a database of
known images in face identification whereas in face
recognition the person’s identity is confirmed by the system
from a stored database of faces. To verify or identify a
person in the digital image, features extracted from the
digital image are compared with features of the images in
the facial database. Face recognition has applications in
legacy systems such as voter registration, passports and
driver’s licenses, crowd surveillance, human-computer
interaction, multimedia management, smart cards, access
control and authentication. Occlusion, pose variation and
illumination problems are still challenging for face
recognition. Face recognition as a biometric has an
advantage that it requires no co-operation of the person
unlike other biometrics.
A problem called as curse of dimensionality exists because
of large number of features. The search space is explored by
the feature selection algorithm to find the optimal feature
subset.Feature selection algorithms can be broadly
classified into wrappers and filters [2], which are
distinguished based on the choice of evaluation metric. A
learning algorithm is used as a part of evaluation function in
the search process of wrapper approaches whereas filters
methods are independent of learning algorithm. Wrapper
methods tend to provide better results and are
computationally intensive than filter methods.
Optimal feature selection (FS) is to find the minimal set of
features from original feature set that can represent the
whole set. Redundant and irrelevant features which have
insignificant effect on the output are removed. Redundant
features are those which doesn’t provide any extra
information than the present and irrelevant features doesn’t
provide any useful information. Feature Selection has
significant role in many applications like biometrics,
machine learning, computer vision, and medical imaging.
Exhaustive search can be done on all combinations possible
with input features to obtain the optimal feature subset but it
is computationally very expensive. With increase in the
dimension of feature space, cost associated for search of
optimal feature subset becomes exponentially high. PSO and
GA are some of the evolutionary computation techniques
which are applicable to the feature selection problems
because of their global search ability.
1.1 Feature Extraction
In face recognition, the first step is feature extraction from
the images. In this paper, feature extraction technique used
is DCT. Information about feature extraction by DCT for
face recognition can be found in [3], [4], [5]. DCT has
strong energy compaction property [6]. DCT can represent
effective features of input image with few coefficients in the
upper left corner of the matrix. Image can be roughly
constructed using these few DCT coefficients.
1.2 Particle Swarm Optimization
Swarm intelligence is a discipline that deals with a system
that is composed of many individuals, that are
homogeneous, self-organized and are coordinated using
decentralized control. The behaviour of the system depends
upon interactions among the individuals and with their
environment.

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PSO is inspired by collective behaviour of social models
such as flock of birds and school of fishes, designed by
Kennedy and Eberhart in 1995. Implementation of PSO is
easier, it has few parameters, it is computationally
inexpensive, and the convergence rate is quicker when
compared with genetic algorithm and genetic programming
[7].PSO possesses the search ability for the best solutions
for optimization problems because of its intelligent
properties like adaptation and self-organizing [8]. Only
primitive mathematical operators are used in PSO. PSO has
an advantage because it is computationally inexpensive and
has low memory requirements [9].
1.2.1 PSO Algorithm
In PSO, a group of particles explores the solution space to
find the best solution. In particle swarm optimization, each
solution is treated as an individual bird in the swarm, i.e., a
particle in the search space. Position of ith
particle is denoted
by Xi = (x1,x2,..,xd) and velocity Vi be (v1,v2,..,vd) in the D
dimensional search space. Each particle uses its individual
experience and its neighbouring particles experience to
move in the search space, that is, every particle has
knowledge about its best position it has found so far and the
best position found by swarm .We denote, PBi as the best
position (pbest) of ith
particle, and gbest as the best position
found by the swarm so far. Every particle is given random
initial position and random initial velocity. Fitness function
evaluates every particle in the search space. It determines
the quality of the particle. Particles are attracted towards
best particles in their search space which have best fitness
values. The velocity and position of particles are updated
according the equation.
(1)
where, i= , N represents the swarm size, rand1 and
rand2 are two random numbers uniformly distributed in the
range 0 and 1, and c1 and c2 are constants. Velocity is
updated based on the inertia of previous velocity, experience
of the particle itself and the experience of the neighboring
particles. Particle position is updated by:
(2)
In every iteration, fitness of every particle is evaluated.
Velocity is updated based on pbest and gbest values and
parameters like c1, c2 and w. Position is updated based on
velocity. This process is repeated until maximum iterations
or error criterion is reached.
1.2.2 Fitness Function
Fitness is a measure that determines the quality or goodness
of a particle. In each iteration every particle is evaluated for
fitness value .The particles quality is evaluated by its ability
to increase class separation. Position vector is represented
by binary values 0’s and 1’s. Let c1.c2,…,cL denote the
classes and N1,N2,…,NL denotes the number of images in
different classes. L be the total number of classes. N is the
total number of images. Let, M1,M2,…ML be the mean of
each class and M0 be the global mean, i.e., mean of all
classes. They are calculated as below.
(3)
(4)
Fitness function is calculated as:
(5)
Pseudo code of PSO algorithm
Initialise Parameters w, c1,c2;
For every particle i
Initialise VelocityVectorVi ;
Initialise PositionVector Xi ;
end
Do until maximum number of iterations
For every particle i
f=fitness(Xi);
If fitness(PBi) < f
PBi=Xi ;
end
If f >fitness(gbest) ;
gbest= Xi;
end
Update Vi;
Update Xi;
end
end
2. RELATED WORK
Some of the recent publications in this topic of research are
briefly reviewed in the following.
Ramadan and Kader [10] has presented a feature selection
algorithm using PSO for face recognition. PSO is applied on
the coefficients (features) of Discrete Cosine Transform
(DCT) and Discrete Wavelet Transform (DWT) to select
the optimum features of image. The optimum subset of
features are selected by PSO based on the fitness function.
ORL database is used for testing the performance. Face
recognition is carried out based on Euclidean distance
between features of unknown face and the features of faces
in the database. The proposed algorithm has better
performance when compared to GA-based feature selection
algorithms in terms of number of features selected and
classification accuracy rate.
Yun et al. [11] proposed feature selection methods using
algorithms like Genetic algorithm and Particle Swarm
Optimisation. An algorithm which makes use of relevance
of features is also proposed. A measure known as minimum
redundancy and maximum relevance (MRMR) is used to
select features, using which redundancy of features is

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minimised and relevance of features is maximised. This
method, when used with genetic algorithm and PSO,
provided quality feature set and achieved better accuracy
rate.
Bing Xue et al. [12] has proposed a binary particle swarm
optimization (BPSO) with two new fitness functions to
minimise the number of features selected and to maximise
the classification accuracy rate. First one linearly changes
weights of fitness function and the other one uses two-stage
fitness function. Experiment results have shown that the
BPSO with the two new fitness functions provides better
results compared to classification performance used as
fitness function.
Unlerand Murat [13] has proposed a modified discrete PSO
algorithm for feature selection. In this approach, the
relevance and dependence of features to be included in the
feature sub-set is dynamically decided by using an adaptive
feature selection algorithm proposed by them. Experimental
results show that it is computationally less expensive and
provides improved recognition rate .
Yuanning Liu et al. [14] has proposed a modified
multiswarm PSO (MSPSO). The particles are split into set
of subsets called sub swarms. Sub swarms are controlled
and monitored by a scheduler. They introduced a
mechanism to decide the survival of sub swarm called as
survival of the fittest. An improved feature selection method
(IFS) has also been designed. The proposed algorithm gave
better results when compared to standard PSO, GA and grid
search in terms of classification accuracy rate.
Liam Cervante et al. [15] has developed two new feature
selection approaches based BPSO. The relevance and
redundancy in the feature subsets is measure by mutual
information and entropy. The two new algorithms are
applied on training data from the dataset and the feature
subset is selected. The subsets’ classification performance is
evaluated by a learning algorithm. The resulting feature set
is found to have less number of features and achieved same
or higher classification rate.
Bing Xue et al.[16] has proposed a new approach for feature
selection based on PSO. In this approach, an external
archive is introduced which stores solutions which are later
used to determine gbest of particles. Particles gbest is
chosen from the solutions in archive using selection
methods such as random selection and roulette wheel
selection. The proposed approaches perform better than the
standard PSO. They select less number of features and have
same or higher classification rate.
From the review given above, it can be concluded that there
is scope for further reduction in feature dimensionality using
new rules for updating the position in PSO algorithm. The
use of features selected by such a modified algorithm is
expected to provide faster computation and improved
recognition performances.
3. MODIFIED PSO ALGORITHM
A modified PSO algorithm has been proposed for feature
selection. Feature selection becomes complex with the
increase in number of features in dataset. Dataset contains
all kinds of features that may be relevant or irrelevant and
redundant. Redundant features decrease the quality of
feature set. With increase in feature number in the dataset,
computational complexity also increases. Optimal features
set is the minimal set of features that provides the optimal
performance. Optimal feature selection using PSO is
proposed to improve the quality of the feature set by
eliminating insignificant features which have very less or
no contribution to outcome, thereby, reducing the number of
features and decreasing computational cost and/or
increasing accuracy of classification rate.
The update mechanism for the position of particle in binary
PSO algorithm is modified to minimize the number of
features and maximize the recognition rate. In the modified
approach, the choice of coefficients is done by incorporating
the average of the mean normalized standard deviations of
various classes and giving more weightage to the lower
indexed DCT coefficients.
Standard deviation for each class is computed as below.
(6)
where n is the number of training images in each class and µ
denotes the mean value of DCT coefficients of training
images in each class and xi denotes DCT coefficients of
each of the training images.
Average of the mean normalized standard deviations of
various classes is calculated as:
(7)
(8)
(10)
where i=1,2,…L. L is the number of classes, total mean is
the sum of all classmeans. Classmean is computed as in
equation 3.
The position of the particle in Modified Binary PSO is
updated by the following equation.
and( , S < median ) (11)
where g,h are matrix indexes, then Xi
t+1
=1 else Xi
t+1
= 0

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The procedure is briefly described in the following:
1. Feature Extraction: DCT coefficients are used as
features.. DCT coefficients on the left upper corner
have significant information. So, only few
coefficients are used in this approach. A 10 × 10
matrix is given as input to BPSO algorithm.
2. Apply Binary PSO algorithm to the subsets of DCT
coefficients obtained from the images in database.
3. Features of the input images which are more
effective are selected. Features which have no
significant contribution to classification accuracy are
removed.
4. Euclidean distance is used for identifying the
images . Face image corresponding to lowest
Euclidean distance is considered as result in this
approach.
Fig-1: Face recognition using PSO
4. EXPERIMENTAL RESULTS
The 2D DCT is applied on input images. DCT coefficients
are used as features. Modified Binary PSO algorithm is
applied to the 10×10 matrix of DCT coefficients obtained
from the images in database. Optimal Feature Set is
obtained based on the fitness function after specified
number of iterations.
Table-1: PSO Parameters
Swarm size 30
w 0.6
c1 2
c2 2
For performance analysis, ORL database is used which
contains 400 images. It contains 10 images for 40 distinct
persons. 4 out of 10 images for each person are used for
training and the rest are used for testing. Each image
resolution is 92 ×112 , with 256 grey levels per pixel.
Table-2: comparison of modified algorithm with [10]
Modified PSO
algorithm
[10]
Number of
training images
240 240
Number of test
images
260 260
Recognition rate 97% 94.8%
Number of
Iterations
50 100
Input Matrix size 10 × 10 50 × 50
Average features
selected
40% 50%
5. CONCLUSION
Optimal feature selection (FS) is to find the minimal subset
of features from original feature set that can represent the
whole set. PSO algorithm explores entire feature set and
finds optimal feature subset. There are many important
factors in PSO, such as the initialization strategy and the
updating mechanisms which can be further analyzed to
improve the performance and increase classification
accuracy rate. .In the modified algorithm, while updating the
position vector , the average of the mean normalized
standard deviations of various classes is considered and
more weightage is given to the lower indexed DCT
coefficients. The modified algorithm when tested on ORL
database has classification accuracy rate of 97% and the
average number of features selected are 40 for a 10 * 10
input matrix.

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