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Face Recognition Using Biogeography Based Optimization

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					                                                                   (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                         Vol. 9, No. 5, May 2011



            Face Recognition Using Biogeography Based
                           Optimization
                Er. Navdeep Kaur Johal                        Er.Poonam Gupta                       Er. Amanpreet Kaur
                       R.I.E.I.T.                                   R.I.E.I.T                               R.I.E.I.T.
             Railmajra, Distt. SBS Nagar                  Railmajra, Distt.SBS Nagar              Railmajra,Distt. SBS Nagar
                   Punjab, India                                 Punjab,India                               Punjab,India
             navdeepkjohal@gmail.com                       poonamjindal3@gmail.com               amanrandhawa85@gmail.com


Abstract: Feature selection (FS) is a global optimization problem in   approaches to face recognitions have been developed; an
machine learning, which reduces the number of features, removes        excellent survey paper on the different face recognition
irrelevant, noisy and redundant data, and results in acceptable        techniques can be found in [1].
recognition accuracy. It is the most important step that affects the
performance of a pattern recognition system. This paper presents a
                                                                                               A. Feature Extraction
novel feature selection algorithm based on Biogeography Based
Optimization (BBO). Biogeography-based optimization (BBO) is a
recently-developed EA motivated by biogeography, which is the          The first step in any face recognition system is the extraction of
study of the distribution of species over time and space. The          the feature matrix. A typical feature extraction algorithm tends
algorithm is applied to coefficients extracted by discrete cosine      to build a computational model through some linear or Non -
transforms (DCT). The proposed BBO-based feature selection             linear transform of the data so that the extracted feature is as
algorithm is utilized to search the feature space for the optimal      representative as possible or when the input data to an algorithm
feature subset where features are carefully selected according to a
                                                                       is too large to be processed and it is suspected to be notoriously
well defined discrimination criterion. Evolution is driven by a
fitness function defined in terms of maximizing the class separation   redundant (much data, but not much information) then the input
(scatter index). The classifier performance and the length of          data will be transformed into a reduced representation set of
selected feature vector are considered for performance evaluation      features (also named features vector). Transforming the input
using the ORL face database. Experimental results show that the        data into the set of features is called feature extraction. If the
BBO-based feature selection algorithm was found to generate            features extracted are carefully chosen it is expected that the
excellent recognition results with the minimal set of selected         features set will extract the relevant information from the input
features.                                                              data in order to perform the desired task using this reduced
                                                                       representation instead of the full size input.
Keywords: Face Recognition, Biogeography Based Optimization, DCT,
Feature Selection
                                                                       Best results are achieved when an expert constructs a set of
                                                                       application-dependent features. Nevertheless, if no such expert
                    I. INTRODUCTION                                    knowledge is available general dimensionality reduction
                                                                       techniques or feature extraction may help. These include:
Face Recognition is a process in which we match the input
image with the given database and produce the output image                      geometrical features extraction
which is similar to the input image. As one of the most                         statistical (algebraic) features extraction [2 - 8].
successful applications of image analysis and understanding,
face recognition has recently received significant attention,          The geometrical approach, represent the face in terms of
especially during the past several years. At least two reasons         structural measurements and distinctive facial features that
account for this trend: the first is the wide range of commercial      include distances and angles between the most characteristic
and law enforcement applications, and the second is the                face components such as eyes, nose, mouth or facial templates
availability of feasible technologies after 30 years of research.      such as nose length and width, mouth position, and chin type.
Even though current machine recognition systems have reached           These features are used to recognize an unknown face by
a certain level of maturity, current systems are still far away        matching it to the nearest neighbor in the stored database.
from the capability of the human perception system. So many            Statistical features extraction is usually driven by algebraic




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                                                                                                      ISSN 1947-5500
                                                                               (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                                     Vol. 9, No. 5, May 2011

methods such as principal component analysis (PCA), and                            maximum number of irrelevant and redundant features obtained
independent component analysis (ICA) [6]. These methods find                       during feature extraction while maintaining acceptable
a mapping between the original feature spaces to a lower                           classification accuracy. Among the various methods proposed
dimensional feature space.                                                         for FS, population-based optimization algorithms such as
                                                                                   Genetic Algorithm (GA)-based method [16-18] and Ant Colony
Alternative algebraic methods are based on transforms such as                      Optimization (ACO)-based method have attracted a lot of
downsampling, Fourier transform (FT), discrete cosine                              attention [19]. In the proposed FR system we utilized an
transform (DCT), and the discrete wavelet transform (DWT).                         evolutionary feature selection algorithm based on swarm
Transformation based feature extraction methods such as the                        intelligence called the Biogeography Based Optimization.
DCT was found to generate good FR accuracies with very low                         Biogeography Based Optimization is explained in the next
computational cost [8].                                                            section.

                            B. Discrete Cosine Transform                                          D. Biogeography based Optimization

DCT has emerged as a popular transformation technique widely                       Biogeography is the study of the distribution of biodiversity
used in signal and image processing. This is due to its strong                     over space and time. It aims to analyze where organisms live,
“energy compaction” property: most of the signal information                       and in what abundance. Biogeography is modeled in terms of
tends to be concentrated in a few low-frequency components of                      such factors as habitat area and immigration rate and emigration
the DCT. The use of DCT for feature extraction in FR has been                      rate, and describes the evolution, extinction and migration of
described by several research groups [9-15]. DCT transforms                        species. Biogeography-Based Optimization (BBO) is a new
the input into a linear combination of weighted basis functions.                   biogeography inspired algorithm for global optimization. BBO
These basis functions are the frequency components of the input                    [20] is a new biogeography inspired global optimization
data.                                                                              algorithm, which is similar to the island model-based GAs [21].
           The general equation for the DCT of an NxM image f                      Each individual is considered as a ‘‘habitat” with a habitat
(x, y) is defined by the following equation:                                       suitability index (HSI) to measure the individual. The variables
                  N 1M 1
F (u,v) (u) (v)   cos
                  x0 y0
                            .u
                           2.N
                                     
                                (2 x1) cos   
                                             .u
                                            2.M
                                                           
                                                 (2 y1) f ( x, y)   ... (i)
                                                                                   of the individual that characterize habitability are called
                                                                                   suitability index variables (SIVs). In BBO, each individual has
                                                                                   its own immigration rate          and emigration rate µ. The
Where f (x, y) is the intensity of the pixel in row x and column y;                immigration rate and emigration rate are functions of the
u= 0, 1,… N-1 and v=0, 1,… M-1 and the functions α(u) , α(v)                       number of species in the habitat. They can be calculated as
are defined as:                                                                    follows:


                            1                                                                        k
                              for u ,v  0                                                   k  I 1                                                … (iii)
     ( u ), ( v )        N
                             2                                       … (ii)                           n
                        
                              for u ,v  0
                             N                                                                        k
For most images, much of the signal energy lies at low                                        k  E                                                   … (iv)
frequencies (corresponding to large DCT coefficient                                                   n
magnitudes); these are relocated to the upper-left corner of the                   where I is the maximum possible immigration rate; E is the
DCT array. Conversely, the lower-right values of the DCT array                     maximum possible emigration rate; k is the number of species of
represent higher frequencies, and turn out to be small enough to                   the kth individual; and n is the maximum number of species.
be truncated or removed with little visible distortion. This means                 Note that Eqs. (iii) and (iv) are just one method for calculating
that the DCT is an effective tool that can pack the most effective                   and µ, there are other different options to assign them based on
features of the input image into the fewest coefficients.                          different species models [20].

                                 C. Feature Selection                              In BBO, there are two main operators, i.e., migration and
                                                                                   mutation. Suppose that we have a global optimization problem
After extracting the features, we further need minimal subset of                   and a population of candidate individuals. The individual is
features so that we are able to recognize the face .Due to this                    represented by a D-dimensional integer vector (SIV). The
reason we need a feature selection algorithm that reduces the                      population consists of NP = n parameter vectors Xi, i = 1. . . NP.




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                                                                                                                  ISSN 1947-5500
                                                                       (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                              Vol. 9, No. 5, May 2011

One option for implementing the migration operator and the                 with a high HSI, and a poor solution represents an habitat with a
mutation operator can be described in Figure 1 and 2,                      low HSI. High HSI solutions resist change more than low HSI
respectively. Where rndreal (0, 1) is a uniformly distributed              solutions. By the same token, high HSI solutions tend to share
random real number in (0,1) and Xi(j) is the jth SIV of the solu-          their features with low HSI solutions. (This does not mean that
 tion Xi. mi is the mutation rate that is calculated as:                   the features disappear from the high HSI solution; the shared
                                                                           features remain in the high HSI solutions, while at the same
                         P            
         mi = mmax   1  i
                      P               
                                                             ...(v)       time appearing as new features in the low HSI solutions. This is
                         max                                             similar to representatives of a species migrating to a habitat,
                                                                           while other representatives remain in their original habitat).
where mmax is an user-defined parameter, and Pmax = arg max Pi,            Poor solutions accept a lot of new features from good solutions.
i = 1,. . ., NP. Each population member has an associated                  This addition of new features to low HSI solutions may raise the
probability, which indicates the likelihood that it was expected a         quality of those solutions. Good solutions have high emigration
priori to exist as a solution to the given problem. The steady             rate and they share their features (SIVs) with bad solutions that
state value for the probability of the number of each species to           have high immigration rate. Additionally, the mutation operator
exist is given by [22]:                                                    tends to increase the diversity of the population. The BBO
                                                                           algorithm can described with the following algorithm in figure
                  1
   P  n       ,                        k 0
     0
                                                                           3:
    1  0 1 k1
           k 1  2    k
                                                                              Pseudo-code for biogeography-based optimization. Here H
                  1
P                                                           ...( )
                                                                 vi           indicates habitat, HSI is fitness, SIV (suitability index variable)
 k
   P               01    k1                                          is a solution feature, denotes immigration rate and µ denotes
     k                                      , 1 k  n
                      n 01    k1                                    emigration rate.
       12    k 1 
                                         
                                          
                       k1 12    k 
                                                                              Biogeography-Based Optimization (BBO)
                                                                             Begin
 The largest possible number of species that the habitat can sup-             /* BBO parameter initialization */
 port is n. It is necessary that μk ≠0 for all k for this limiting               1. Create a random set of habitats (population)
probabilities to exist.                                                             H1,H2, . . . ,Hn;
 1: for i = 1 to NP                                                              2. Compute corresponding HSI values;
                                                                              /* End of BBO parameter initialization */
 2: Select Xi with probability α           i                                    3. While not T /* T is a termination criterion */
 3:   if rndreal (0, 1) <   i   then                                              4. Compute immigration rate       and emigration
 4:    for j = 1 to NP do                                                            rate µ for each habitat based on HSI;
 5:     Select Xj with probability α µj                                           /* Migration */
 6:       if rndreal (0, 1) < µj then                                             5. Apply migration as defined in algorithm 1.
 7:         Randomly select an SIV σ from Xj                                      /* End of migration */
 8:         Replace a random SIV in Xi with σ                                     /* Mutation*/
 9:      end if                                                                   6. Apply mutation as defined in algorithm 2.
 10:    end for                                                                   /* End of mutation */
 11: end if                                                                     7. Recompute HSI values;
 12: end for                                                                    8. End while
                 Figure.1: Algorithm for Habitat Migration.                     9. End
 1: for i = 1 to NP
 2: Compute the probability Pi
 3: Select SIV Xi(j) with probability α Pi                                                       Figure 3: Main BBO Algorithm
 4: if rndreal (0, 1) < mi then
 5:     Replace Xi(j) with a randomly generated                                   II. BBO-BASED FEATURE SELECTION
         SIV
 6: end if
                                                                           In this proposed work, features of image are extracted using
 7:end for
                                                                           DCT technique. The extracted features are reduced further by
                  Figure.2: Algorithm for Habitat Mutation                 using Biogeography Based Optimization to remove redundancy
With the migration operator, BBO can share the information                 and irrelevant features. The resulting feature subset (obtained by
between solutions. A good solution is analogous to an habitat              BBO) is the most representative subset and is used to recognize




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                                                                                                           ISSN 1947-5500
                                                                                    (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                                                Vol. 9, No. 5, May 2011

the face from face gallery.                                                                                                D. Classifier

                                  A. Habitat Representation                             After the training phase, a typical and popular Euclidean
                                                                                        distance is employed to measure the similarity between the test
In proposed work, each habitat represents one possible solution                         vector and the reference vectors in the gallery. Euclidean
(feature subset) required for face recognition. Each of the                             distance is defined as the straight-line distance between two
features extracted by DCT of image represents one Suitability                           points. For N-dimensional space, the Euclidean distance
Index Variable (SIV) of the habitat. Further, during feature                            between two any points’ pi and qi is given by:
subset selection each of these feature is either selected or
rejected, SIVЄ C is an integer and C  [0, 1]. A habitat H Є                                     N
SIVm where m is the length of the feature vector extracted by the
DCT.
                                                                                          D    (p
                                                                                                 i 1
                                                                                                         i    qi ) 2                           …(x)

                                                                                        Where pi (or qi) is the coordinate of p (or q) in dimension i.
                                             B. SIV Mutation
                                                                                               E. Proposed BBO-Based Feature Selection Algorithm
In proposed work, a habitat is chosen for mutation based on
mutation rate and species count probabilities defined in (4) and                        In the proposed work (figure 4), the features of image are
(5). Once a habitat is selected for mutation, a random SIV is                           extracted using DCT technique. These extracted features are
selected; it is mutated to 0 if its value is 1 or vice versa.                           further reduced (or selected) using BBO. In BBO, each SIV of
Therefore, if a particular feature was earlier selected, it is                          habitats is randomly set to either 0 or 1 initially, which implies
rejected after mutation and vice versa.                                                 that initial feature subset selection is done randomly but after the
                                                                                        completion of BBO algorithm, BBO helps to select the optimal
                                C. Habitat Suitability Index                            set of features from the given features. The stopping criterion of
                                                                                        proposed algorithm is number of iterations. At the end of
In each generation, each habitat is evaluated, and a value of                           training phase, we have the optimal set of features. These
goodness or fitness is returned by a fitness function. This                             features are then selected from the test image and the face
evolution is driven by the fitness function F that evaluates the                        gallery. The test image is recognized as that face from face
quality of habitat in terms of their ability to maximize the class                      gallery which has minimum Euclidean distance from the test
separation term indicated by the scatter index among the                                image on the basis of these selected features.
different classes [23]. Let w1, w2 ..., wL and N1, N2,..., NL denote
the classes and number of images within each class,                                              1.     Feature Extraction: Obtain the DCT array by applying
respectively. Let M1 ,M2 ,..., ML and M0 be the means of                                                Discrete Cosine Transformation to image.
                                                                                                 2.     Take the most representative features of size nxn from
corresponding classes and the grand mean in the feature space,                                          upper left corner of DCT Array.
Mi can be calculated as:                                                                         3.     Feature Selection:
                                                                                                        Apply the BBO algorithm defined in algorithm 3 to
                                  Ni                                                                    obtain the feature subset of the extracted features.
                         1
         Mi 
                         Ni
                                Wj 1
                                             j
                                              (i )
                                                     , i  1,2,...., L   … (vii)
                                                                                                 4.     Pick up the habitat H with max (HSI) value. The SIVs
                                                                                                        of this habitat H represent the best feature subset of the
                                                                                                        features defined in step 2.
             (i )                                                                                       (Feature Selection Ends)
Where   W   j       , j=1,2,…,Ni , represents the sample image from                              5.     Classification: calculate the difference between the
class wi and grand mean M0 is:                                                                          feature subset (obtained in step 4) of each image of
                                                                                                        facial gallery and the test image with the help of
                                                                                                        Euclidean Distance defined in formula (x). The index
                            L
                    1                                                                                   of the image which has the smallest distance with the
        M0 
                    N
                           N M
                           i 1
                                         i     i                         … (viii)                       image under test is considered to be the required index.

 Where N is the total no of images of all the classes. Thus the                                 Figure 4: Face Recognition using BBO based Feature Selection
 between class scatter fitness function F is computed as follow:
                     L                                                                                       III. EXPERIMENTAL RESULTS
      F             (M
                    i 1
                                   i    M 0 ) (M i  M 0 )
                                                      t
                                                                          … (ix)
                                                                                        The performance of the proposed feature selection algorithm is
                                                                                        evaluated using the standard Cambridge ORL gray-scale face




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                                                                                                                           ISSN 1947-5500
                                                                (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                         Vol. 9, No. 5, May 2011

database. The ORL database of faces contains a set of face                             TABLE II. Results of BBO-FS algorithm
images taken between April 1992 and April 1994 at the AT&T
                                                                       DCT             Number of      Average       Training      Average
Laboratories (by the Oliver Research Laboratory in Cambridge,          Feature         Features       no.      of   time (in      Recogniton
UK) [24] and [25]. The database is composed of 400 images              Vector          input    to    features      seconds)      Rate
corresponding to 40 distinct persons. The original size of each        Size            BBO-FS         selected
image is 92x112 pixels, with 256 grey levels per pixel. Each                                          by BBO -
                                                                                                      FS
subject has 10 different images taken in various sessions varying      20X20           400            219           85.743        100%
the lighting, facial expressions (open/ closed eyes, smiling/ not      30X30           900            451           95.166        100%
smiling) and facial details (glasses/ no glasses). All the images      40X40           1600           814           136.178       100%
were taken against a dark homogeneous background with the              50X50           2500           1243          165.101       100%
subjects in an upright, frontal position (with tolerance for some
side movement). Four images per person were used in the             For each of the problem instance (20X20, 30X30, 40X40, and
training set and the remaining six images were used for testing.    50X50), algorithm is run 5 times and each time, random test
                                                                    image is chosen to be matched with face gallery. The test face
                     TABLE I. BBO parameter setting                 matches with image in face gallery in each trial and average
                                                                    recognition rate is 100 % for each problem instance. The BBO-
     Size of ecosystem (No of Habitats)               30
                                                                    selection algorithm reduces the size of original feature vector to
     Number of iterations of BBO algorithm            100           52%, 50%, 50.7%, and 50% for problem instance of 20X20,
                                                                    30X30, 40X40, and 50X50 respectively. For example, if the
                                                                    DCT of an image is calculated and 20X20 DCT subset is taken
     SIV value                                        0 or 1        from upper left of DCT array, there are total 400 features which
                                                                    are given as an input to BBO-FS algorithm. BBO-FS reduces
                                                                    the 400 features to 219 which means only 219 features are
In this work, we test the BBO-based feature selection algorithm
                                                                    required to recognize the face from facial gallery.
with feature vectors based on various sizes of DCT coefficient.
The 2-dimentional DCT is applied to the input image and only a
subset of the DCT coefficients corresponding to the upper left
corner of the DCT array is retained. Subset sizes of 50x50,                                      IV. CONCLUSION
40x40, 30x30 and 20x20 of the original 92x112 DCT array are
used in this work. Each of 2- dimensional subset DCT array is       In this paper, a novel BBO-based feature selection algorithm for
converted to a 1-dimensional array using raster scan. This is       FR is proposed. The algorithm is applied to feature vectors
achieved by processing the image row by row concatenating the       extracted by Discrete Cosine Transform. The algorithm is
consecutive rows into a column vector. This column vector is        utilized to search the feature space for the optimal feature
the input to the subsequent BBO-feature selection algorithm.        subset. Evolution is driven by a fitness function defined in terms
                                                                    of class separation. The classifier performance and the length of
To calculate average recognition rate for each problem instance     selected feature vector were considered for performance
(20X20, 30X30, 40X40, and 50X50 DCT Array), test image is           evaluation using the ORL face database. Experimental results
randomly chosen from 40 classes. Five trials are taken for each     show the superiority of the BBO-based feature selection
problem instance. Average recognition is measured by knowing        algorithm in generating excellent recognition accuracy with the
how many times correct faces were identified out of 5 trials (for   minimal set of selected features.
each problem instance). The average recognition rate is
measured together with the CPU training time and the average                                    REFERENCES
number of selected features for each problem instance. The
algorithm has been implemented in Matlab 7 and the result for       [1] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face Recognition:
                                                                    A Literature Survey”, ACM Computing Surveys, vol. 35, no. 4, pp. 399-458,
each problem instance is shown in table 2:                          2003.
                                                                    [2]R. Brunelli and T. Poggio, “Face Recognition: Features versus Templates,”
                                                                    IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp.
                                                                    1042-1052, 1993.
                                                                    [3] C. Liu and H. Wechsler, “Evolutionary Pursuit and Its Application to Face
                                                                    Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22,
                                                                    no. 6, pp. 570-582, 2000.




                                                                130                                  http://sites.google.com/site/ijcsis/
                                                                                                     ISSN 1947-5500
                                                                            (IJCSIS) International Journal of Computer Science and Information Security,

                                                                                                                                     Vol. 9, No. 5, May 2011

[4] M. A. Turk and A. P. Pentland, “Face Recognition using Eigenfaces,” Proc.    [23] C. Liu and H. Wechsler, “Evolutionary Pursuit and Its Application to
of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-591,      Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol.
June 1991.                                                                       22, no. 6, pp. 570-582, 2000.
[5] L. Du, Z. Jia, and L. Xue, “Human Face Recognition Based on Principal        [24] F. M. Matos, L. V. Batista, and J. Poel, “Face Recognition Using DCT
Component Analysis and Particle Swarm Optimization -BP Neural Network,”          Coefficients Selection,” Proc. of the 2008 ACM Symposium on Applied
Proc 3rd Conference. on Natural Computation (ICNC 2007),vol. 3, pp. 287-291,     Computing, (SAC’08),pp. 1753-1757, March 2008.
August 2007.                                                                     [25] H. R. Kanan, K. Faez, and M. Hosseinzadeh, “Face Recognition System
[6] X. Yi-qiong, L. Bi-cheng and W. Bo, “Face Recognition by Fast Independent    Using Ant Colony Optimization-Based Selected Features,” Proc. IEEE Symp.
Component Analysis and Genetic Algorithm,” Proc. of the 4th International        Computational Intelligence in Security and Defense Applications (CISDA 2007),
Conference on Computer and Information Technology (CIT’04), pp. 194-198,         pp 57-62, April 2007.
Sept. 2004.
[7] X. Fan and B. Verma, “Face recognition: a new feature selection and                                     AUTHORS PROFILE
classification technique,” Proc. 7th Asia-Pacific Conference on Complex
Systems, December 2004.
[8] A. S. Samra, S. E. Gad Allah, R. M. Ibrahim, “Face Recognition Using
Wavelet Transform, Fast Fourier Transform and Discrete Cosine Transform,”                                Navdeep Kaur has done B-Tech (Hons.)
Proc. 46th IEEE International Midwest Symp. Circuits and Systems
                                                                                                         in Computer Science & Engineering &
(MWSCAS'03), vol. 1, pp. 272- 275, 2003.
[9] A. S. Samra, S. E. Gad Allah, R. M. Ibrahim, “Face Recognition Using                                 scored 81% marks from Punjab
Wavelet Transform, Fast Fourier Transform and Discrete Cosine Transform,”                                Technical University, Jalandhar (India)
Proc. 46th IEEE International Midwest Symp. Circuits and Systems                                         in 2005 and MTech in Computer Science
(MWSCAS'03), vol. 1, pp. 272- 275, 2003.
                                                                                                         & Engineering from Guru Nanak Dev
[10] Z. Yankun and L. Chongqing, “Efficient Face Recognition Method based
on DCT and LDA”, Journal of Systems Engineering and Electronics, vol. 15,                                Engineering College, Ludhiana of India
no. 2, pp. 211-216, 2004.                                                                                in 2009. She is currently working as a
[11] C. Podilchuk and X. Zhang, “Face Recognition Using DCT-Based Feature        lecturer in computer science and IT department of Rayat
Vectors,” Proc. IEEE International Conference on Acoustics, Speech and Signal
                                                                                 institute of Engineering & Information Technology of India.
Processing (ICASSP’96), vol. 4, pp. 2144-2147, May 1996.
[12] F. M. Matos, L. V. Batista, and J. Poel, “Face Recognition Using DCT
Coefficients Selection,” Proc. of the 2008 ACM Symposium on Applied
Computing, (SAC’08),pp. 1753-1757, March 2008.                                                        Er.Poonam Gupta has done BTech(First
[13] M. Yu, G. Yan, and Q.-W. Zhu, “New Face recognition Method Based on
                                                                                                      Division) in Information Technology &
DWT/DCT Combined Feature Selection,” Proc. 5th International Conference
on Machine Learning and Cybernetics, pp. 3233-3236, August 2006.                                      scored 67.64% marks from Kurukshetra
[14] Z. Pan and H. Bolouri, “High Speed Face Recognition Based on Discrete                            University, Kurukshetra(India) in 2007
Cosine Transform and Neural Networks,” Technical Report, Science and                                  and M.Tech in Computer Science &
Technology Research Center (STRC), University of Hertfordshire.
                                                                                                      Engineering from Rayat institute of
[15] Z. M. Hafed and M. D. Levine, “Face Recognition Using Discrete Cosine
Transform”, International Journal of Computer Vision, vol. 43, no. 3, pp. 167-                        Engineering & Information Technology,
188. 2001                                                                                             Railmajra of India in 2011. She is
[16] X. Fan and B. Verma, “Face recognition: a new feature selection and         currently working as a lecturer in computer science and IT
classification technique,” Proc. 7th Asia-Pacific Conference on Complex
Systems, December 2004.
                                                                                 department of Rayat Polytechnic college (Evening shift) of
[17] D.-S. Kim, I.-J. Jeon, S.-Y. Lee, P.-K. Rhee, and D.-J. Chung, “Embedded    Punjab, India. She has presented 3 papers in National
Face Recognition based on Fast Genetic Algorithm for Intelligent Digital         Conferences.
Photography,” IEEE Trans. Consumer Electronics, vol. 52, no. 3, pp. 726-734,
August 2006.
[18] M. L. Raymer, W. F. Punch, E. D. Goodman, L.A. Kuhn, and A. K Jain,                                    Amanpreet Kaur has done B.Tech in
“Dimensionality Reduction Using Genetic Algorithms,” IEEE Trans.                                            Computer Science & Engineering
Evolutionary Computation, vol. 4, no. 2, pp. 164-171, July 2000.                                            and scored 73% marks from Punjab
[19] H. R. Kanan, K. Faez, and M. Hosseinzadeh, “Face Recognition System
                                                                                                            Technical University, Jalandhar
Using Ant Colony Optimization-Based Selected Features,” Proc. IEEE Symp.
Computational Intelligence in Security and Defense Applications (CISDA 2007),                               (India) in 2007 and M.Tech in also
pp 57-62, April 2007                                                                                        the same stream from Rayat
[20] D. Simon, “Biogeography-based optimization”, IEEE Transactions on                                      Institute   of    Engineering     &
Evolutionary Computation, vol. 12, no. 6, pp. 702-713, 2008.
                                                                                                            Information Technology,Railmajra,
[21] D. Whitley, S. Rana, R.B. Heckendorn, “The island model genetic
algorithm: on separability, population size and convergence”, Journal of                                    Punjab,India in 2010. She scored
Computing and Information Technology 7 (1998) 33–47.                                                        71% marks in her post graduation.
[22] Gong, W., Cai, Z., Ling, C.X. ,Li, H., “A real-coded biogeography-based     She is working as a lecturer in Computer Science & I.T.
optimization with mutation”, Applied Mathematics and Computation, vol. 216,
                                                                                 department in Rayat Institute of Engineering & information
no. 9, pp. 2749–2758,201
                                                                                 technology , Railmajra, Punjab, India.




                                                                             131                                  http://sites.google.com/site/ijcsis/
                                                                                                                  ISSN 1947-5500

				
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