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Journal of Theoretical and Applied Information Technology30th June 2016. Vol.88. No.3

© 2005 - 2016 JATIT & LLS. All rights reserved.

ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195

607

RECOGNIZING GENDER THROUGH FACIAL IMAGE USINGSUPPORT VECTOR MACHINE

1FITRI DAMAYANTI, 2AERI RACHMAD1,2Faculty of Engineering, University of Trunojoyo Madura, Indonesia

E-mail: [email protected]

ABSTRACT

The face is one part of the human body that has special characteristics, which is often used to distinguishthe identity of one individual and another. Facial recognition is very important to be developed since thisapplication is applied in the security system. The recognition of sex is one part of the face recognition.Gender plays an important role in our interactions in the community and with the computer. Classificationgender of the face image can be applied in the field of demographic data collection, human-computerinterface (customize the behavior of software in connection with the sex of the user) and others. Thepurpose of this study is to make implementation of the system in recognizing the gender on facial image orfilling the form with the Gender Recognition face image that is able to recognize a person's sex quickly andaccurately, and run well. This study used methods of Two Dimensional Linear Discriminant Analysis(TDLDA) for feature extraction, which directly assess within-class scatter matrix of the transformationmatrix without any image into a vector image, and this resolves the singular problem within-class scattermatrix. To obtain optimal recognition results of the classification method, it used the classification SupportVector Machine. This study integrates TDLDA and SVM methods for the introduction of gender based onfacial image. The combination of both methods proves the optimal results with an accuracy of 74% to 92%with a test that uses a database of faces taken from http://www.advancedsourcecode.com.

Keywords: Support Vector Machine, Two Dimensional Linear Discriminant Analysis, Gender

1. INTRODUCTION

The face is one of the easiest physiologicalmeasure and often used to distinguish the identityof one individual to another. The human brain hasthe ability to recognize and distinguish betweenthose which face each other with a relatively quickand easy. Face recognition man is one field that isdeveloping today. Application of face recognitioncan be applied in the field of security system suchas room access permission.One part of face recognition that has beendeveloped is the recognition of sex (genderrecognition). It has similarities between genderrecognition and face recognition that lies in itsextraction process. However, there is still a littledifference in the classification process. All of theserecognitions which are used to calculate how manypeople are male or female who are coming to astore or a public agency are still processedmanually, so it takes a longer time. To facilitatewhat advertisements are displayed on electronicbillboards in public places or roadside, it can beadjusted with the sex of the person who passed theadvertisements. That is why, this software for theintroduction of gender based on facial image was

created to facilitate and speed up the processingtime.The difficulties in the process of gender recognitionis mainly because of the complexities of thecondition of the face, such as the position of theimage, lighting and expression of different imagesthat have a high dimension that must go through theprocess of the compression / extraction prior to thedata processed by the method of classification.The previous research which was associated withthis research is the research conducted by BurhanErgen and Serdar Abut entitled "GenderRecognition Using Facial Images". In that study, itconducted the recognition of gender-based facialimage using GLCM. The trial results have anaccuracy rate of 60% by using Face FEI databaseconsisting of 100 female face images and the facialimage 100 men [1].A research which was conducted by VladimirKhryashchev, Andrey Priorov, Lev Shmaglit andMaxim Golubev entitled "Gender Recognition ViaFace Area Analysis", provides the recognition ofgender-based facial image using Adaptive Featureand SVM method. The trial results have anaccuracy rate of 90.8% using ferrets database [2].




608

Another research which was conducted by NafinFenanda, Rima Tri Wahyuningrum, FitriDamayanti entitled "Introduction to Gender BasedFacial Image Method Using Local Binary Pattern(LBP) and Fisherface", presented the recognition ofgender-based facial image using LBP andFisherface. The trial results have the highest degreeof accuracy by 75% by using a database drawnfrom http://www.advancedsourcecode.com [3].The last research which was conducted by FitriDamayanti entitled "Introduction to Gender BasedFacial Image Method Using Two-DimensionalLinear Discriminant Analysis", proved therecognition of gender-based facial image usingTDLDA and ED. The trial results have the highestdegree of accuracy by 89% by using a databasetaken from http://www.advancedsourcecode.com[4].

This study integrates LDA and SVM for theintroduction of gender-based facial image. TDLDAis used as a feature extraction method that directlyassess within-class scatter matrix of images withoutimage transformation matrix into a vector.Moreover, it is used to overcome the problem ofsingular matrices within-class scatter. TDLDAwears fisher criterion to find the optimaldiscriminating projections which are obtained fromall the features of the selected faces which arelooking eigen-values and the greatest eigenvectors.Classification method which is used is the classifierSupport Vector Machine (SVM). SVM classifieruses a function or hyperplane to separate the twoclasses of patterns. SVM will try to find the optimalhyperplane in which two classes of patterns can beseparated to the maximum.

2. SYSTEM DESIGN

Broadly speaking, this system consists of twoparts, namely the image of the training process andthe testing process. In Figure 1, it is an outline ofthe picture recognition system based on the sex ofthe face image. In the training process TDLDAthere is a process used to extract features, thefeatures that are selected during the training processused in the classification process and is also used toget features that are selected in the trial data. Eachface data base used is divided into two, partly usedfor training process and the rest was used for thetesting process.

Figure 1. System Introduction to Gender BasedFacial Image.2.1 Feature extractionExtraction feature in the training process is doneusing Two-Dimensional Linear DiscriminantAnalysis. This stage aims to get the features that areselected from the data enter training. These featuresare selected to obtain from all the facial features,look for eigen-values and eigen-vectors greatest.Features that are selected will be used for theclassification process is used for training andtesting data feature extraction.Extraction feature in the testing process is done bytaking the feature extraction results on the trainingprocess which was applied to the test data. Featureextraction results on this test data is used as input tothe classification process testing.

2.2 Algorithm Design TDLDAHere are the steps in the process TDLDA against adatabase of training images [5]:1. If a database of facial images are a set of ntraining image Ai = [A1, A2, ..., An] (i = 1,2, ..., n)with dimensions of image (RXC), then the total setof all the image matrix are:

An =

rcnrnrn

cnnn

cnnn

AAA

AAAAAA

)(2)(1)(

2)(22)(21)(

1)(12)(11)(

...............

...

...(1)

2. Determining the value 1 (dimension projectionlines) and 2 (dimensional projection column). ≤value ≤ r and c.3. The next step is the calculation of the averageimage of classroom training to i:

iXi

i Xn

M 1 (2)

4. Calculate the average of all training image:




609

M =n1

k

i X iX

1(3)

5. Establish the transformation matrix R size (c,2 )

which is obtained from a combination of theidentity matrix size ( 2 ,

2 ) with zero matrix size(c- 2 , 2 ).6. Calculate the between class scatter matrix R inaccordance with equation (4).S R

b = Ti

Tk

iii MMRRMMn )()(

1

the size of the

matrix (r x r) (4)7. Counting within class scatter matrix R inaccordance with equation (5).S R

W = ,)()(1

Ti

Ti

k

i xMXRRMX

i

the size of the

matrix (r x r) (5)8. Calculate the generalized eigenvalue ( i ) of SRb and S R

W using SVD in accordance with equation(6)J4(L) = maxtrace((LTS R

W L)-1(LTS Rb L)), maxtrace

size of the matrix (r x r) (6)9. Take as much eigenvector 1 from step 8 as a

transformation matrix of rows (L). L = [L

1 , ...,L1

] as the size of the matrix (r x 1 ).10. Calculate the range class scatter matrix Laccording to equation (7).S L

b = )()(1

MMLLMMn iTT

k

iii

,the size of the

matrix (c x c). (7)11. Counting within class scatter matrix Laccording to equation (8).S L

W = ),()(1

iTT

i

k

i xMXLLMX

i

the size of

the matrix (c x c). (8)12. Calculate the generalized eigenvalue ( i ) of SLb andS L

W using SVD in accordance with equation(9).J5(R)=maxtrace((RTS L

W R)-1(RTS Lb R)),the size of the

matrix (c x c). (9)13. Take as much eigenvector 2 of step 12 as thetransformation matrix column (R). R = [ R

1 , ...,R

2 ], the size of the matrix (c x 2 ).14. Calculate the extraction feature matrixBi=LTAiR, the size of the matrix (

1 x2 )

15. Output: Bi extraction feature matrix, linematrix transformation L, and the transformationmatrix column R.

2.3 ClassificationThe classification of SVM is divided into

two processes, namely the processes of training andtesting process. In the SVM, training process usesthe feature matrix which is generated in theextraction process as input features. While in testSVM, utilizing the projection matrix is generated inthe process of feature extraction which is thenmultiplied by the test data (test samples) as input.The classification of SVM for Multiclass OneAgainst All will build a number of binary SVM k(k is the number of classes). The decision has afunction that has a maximum value, indicating thatthe data xd is the members of the class of functionsof that decision. Block diagram SVM training andtesting process is shown in Figure 2 [6].

Figure 2. Block diagram of the process of trainingand classification using SVM.

Training data that has been projected by TDLDA,then became the SVM training data. If thedistribution of the data generated in the processTDLDA have a linear distribution, then one of themethods used SVM is used to classify these datawhich is used to transform data into dimensionalfeature space, so it can be separated linearly on thefeature space. Because the feature space in practice




610

usually has a higher dimension of the input vector(input space). This resulted in computing thefeature space may be very large, because there isthe possibility of feature space can have a numberof features that are not infinite. So on SVM used"kernel trick". Kernel functions used in this study isa Gaussian shown in equation 10. [7]

K(x,y)=exp ))2(||( 2

2

yx

. (10)

A number of support vector at each training data tolook for to get the best solution interface. The issueof the best interface solutions can be formulated inequation 11.

l

iijijiji

l

ii xxyyQ

1,1 21)( , (11)

Where : 0),...,2,1(01

i

l

iii yli .

Data ix were correlated with αi> 0 iscalled a support vector. Thus, it can be obtained thevalue which will be used to find w. Solutioninterface obtained by the formula: w =Σαiyixi; b= yk- wTxkfor every xk, withαk0.The testing process or classification is carried outon every binary SVM use the value w, b, and xigenerated in the training process in any binarySVM. The resulting function for the testing processis shown in Equation 12.

,),( iidii bwxxKf (12)where: i = 1 to k; xi = support vector; xd = test data.The output is in the form of an index i with thegreatest fi which is a class of test data.

2.4 Data UsedTesting the system in this study uses the

test data a 200x200 pixel image 400 of 200 imagesof 200 male and female image. Trial data are takenfrom http://www.advancedsourcecode.com and hasbeen used in previous studies. The data is the dataof these trials tested testing using SVMclassification with training data. Figure 3 showsseveral examples of facial images that are used as adata testing and training data.

Figure 3. Example Of Facial Imagery Used For DataTrial

Scenario experiments performed in thisstudy are divided into eight scenarios, the scenario1, scenario 2 scenario 3, scenario 4, 5 scenario,scenario 6, 7 scenario, the scenario 8. Thedifference of those eight scenarios lie in the amountof training data and data testing used. More detailscan be seen in Table 1.

Table 1 Simulation Scenarios In SystemScenario Data Training Data Testing

1 300images

150 Female150 Male

100images

50 Female50 Male

2 280image

140 Female140 Male

120images

60 Female60 Male

3 260image

130 Female130 Male

140images

70 Female70 Male

4 240images

120 Female120 Male

160images

80 Female80 Male

5 160images

80 Female80 Male

240images

120 Female120 Male

6 140images

70 Female70 Male

260images

130 Female130 Male

7 120images

60 Female60 Male

280images

140 Female140 Male

8 100images

50 Female50 Male

300images

150 Female150 Male

3. EXPERIMENTS AND RESULTS

The methods which are used in this test aredivided into three groups. The first group uses theLocal Binary Pattern (LBP) for preprocessing, it isFisherface method for feature extraction andclassification methods Euclidean Distance (ED) asthe research done by Nafin Fenanda, Rima TriWahyuningrum, Damayanti Fitr [3]. The secondgroup uses methods TDLDA as feature extractionand classification methods Euclidean Distance as[4]. The third group uses methods TDLDA asfeature extraction and SVM as the classificationmethod. The third group was a study done byresearchers. Table 2 shows the comparison of thetest results from previous studies and researchundertaken by researchers.

Table 2 Comparison of Results of Testing




611

Table 2 shows that the percentageintroduction TDLDA - SVM is higher than themethod TDLDA-ED and methods LBP-Fisherface-ED. It would be easier to see the differencebetween the trial results TDLDA-SVM methodwith other methods by using bar charts. Figure 4shows the results of testing against a databasewhich is taken fromhttp://www.advancedsourcecode.com to see thedifferences of TDLDA-SVM method to the othermethods.

Figure 4. Graph recognition success rate for each testvariations on Database taken from

http://www.advancedsourcecode.comu using TDLDA-SVM method and other methods.

The excellence of TDLDA method – SVMis compared to other methods like follows:

i. TDLDA - SVM compared with TDLDA - ED.In the ED, it did not concern to the distribution ofthe data which is only based on the distance of thenew data into multiple data / nearest neighbor. Itcould be data / nearest neighbor which was not agroup, so that the resulting classification is wrong.In SVM, the distribution of data is trying to find afunction separator (classifier) which is optimumthat can separate the two sets of data from twodifferent classes. Each class has a different patternand is separated by a dividing function, so that ifthere is new data that will be known, the classes areclassified according to the new data. Thus theresulting classification is more perfect than theother classification methods.

ii. TDLDA - SVM compared with Fisherface.In Fisherface pre-processing procedures to reducethe dimension using PCA may cause the loss ofsome important discriminant information for LDAalgorithm is applied after PCA.

In TDLDA take full advantage of the informationthat is discriminatory on the scope of the face (facespace), and did not throw some subspace whichmay be useful for the introduction.From the results of experiments which wereconducted, there were some incorrect recognition,like women were recognized as men in therecognition results, and vice versa. Somerecognition that were caused by several factors,namely the shape of the head, the hair shape andexpression between the image of women and men.

4. CONCLUSION

From the experiments that have been done can bedrawn conclusions as follows:1. Method TDLDA - SVM was able to show that

the optimal recognition accuracy which iscompared to other methods (TDLDA - ED, LBP -Fisherface - ED). This is because the singularTDLDA is able to overcome the problem, tomaintain the existence of discriminatoryinformation, and to maximize the distancebetween classes and minimizes inter-classdistance. While SVM has the ability to discoverthe function of separator (classifier) which isoptimum.

2. There are three important variables that affect thesuccess rate of introduction, that is the use ofsequence variations of training samples per class,the use of the number of training samples perclass, and the number of dimensions ofprojection.

3. From the test results using TDLDA-SVMmethod, it obtained recognition accuracy rate ofbetween 74% to 92%.

4. Incorrect classification of the trials was caused bythe head shape, the hair style and faceexpressions between the image of women andmen.

5. SUGGESTIONThis research will be continued to recognizephrases that will be applied in the form of multiface images. In addition to the field of digitalimage processing.

REFERENCES:

[1]. Ergen B, Abut S. Gender Recognition UsiangFacial Images. International Conference onAgriculture and Biotechnology. 2013. Vol 60 :112-117.

0

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reco

gniti

on in

Per

cent

age

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[2]. Khryashchev V, Priorov A, Shmaglit L, andGolubev M. Gender Recognition Via FaceArea Analysis. Proceedings of The WorldCongress on Engineering and CompuerScience. 2012. Vol 1 .

[3]. Fenanda N, Wahyuningrum RT, Damayanti F.Introduction of Gender Based Facial ImageMethod Using Local Binary Pattern (LBP) andFisherface. Bangkalan. InformaticsEngineering University of Trunojoyo Madura.2015.

[4]. Damayanti F. Introduction of Gender BasedFacial Image Method Using Two-DimensionalLinear Discriminant Analysis. NationalConference Nasional System & Informatics.2015.

[5]. Quan XG, Lei Z, and David Z. FaceRecognition Using FLDA With SingleTraining Image Per Person. AppliedMathematics and Computation. 2008. Vol 205: 726-734.

[6]. Burges JC. A Toturial on Support VectorMachines for Pattern Recognition, DataMining and Knowledge Discovery. 2 (2) : 955-974. 1998.

[7]. Hsu, Chih-Wei,Chih-Jen Lin. A Comparison ofMethods for Multi-class Support VectorMachines. IEEETransactions on Neuralnetwork. 13(2) : 415-425. 2002.

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