research article personal authentication using...

Research ArticlePersonal Authentication Using MultifeaturesMultispectral Palm Print Traits

Gayathri Rajagopal and Senthil Kumar Manoharan

Department of Electronics and Communication Engineering Sri Venkateswara College of EngineeringSriperumbudur Taluk Tamil Nadu 602117 India

Correspondence should be addressed to Gayathri Rajagopal gayathricontactgmailcom

Received 23 March 2015 Revised 25 May 2015 Accepted 31 May 2015

Academic Editor Christian Wallraven

Copyright copy 2015 G Rajagopal and S K Manoharan This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Biometrics authentication is an effective method for automatically recognizing a personrsquos identity with high confidenceMultispectral palm print biometric system is relatively new biometric technology and is in the progression of being endlessly refinedand developed Multispectral palm print biometric system is a promising biometric technology for use in various applicationsincluding banking solutions access control hospital construction and forensic applications This paper proposes a multispectralpalm print recognition method with extraction of multiple features using kernel principal component analysis and modified finiteradon transform Finally the images are classified using LocalMeanK-Nearest CentroidNeighbor algorithmTheproposedmethodefficiently accommodates the rotational potential deformations and translational changes by encoding the orientation conservingfeatures The proposed system analyses the hand vascular authentication using two databases acquired with touch-based andcontactless imaging setup collected from multispectral Poly U palm print database and CASIA database The experimental resultsclearly demonstrate that the proposed multispectral palm print authentication obtained better result compared to other methodsdiscussed in the literature

1 Introduction

The field of hand vascular pattern investigation has inviteda lot of concentration for forensic and civilian usage assuggested by Jain and Feng [1] Multispectral palm printtechnology uses subcutaneous vascular network on the handto identify personals in biometric application The patternof multispectral palm print is unique to each personal evenbetween identical twins Therefore the pattern of multispec-tral palm print is a highly distinctive feature that may be usedfor authenticating the personal as suggested by MacGregorand Welford [2] Multispectral palm print biometric tech-nology is relatively new and it is in the progression of beingendlessly refined and developed

Since the introduction of multispectral palm print tech-nology a number of efforts have been put to extend toother multispectral pattern technologies such as multispec-tral finger print retinal pattern Although multispectral palmprint technology is an ongoing immobile research area ofbiometric system a large number of units have been installed

in many applications such as security time and attendanceaccess control and hospitals compared to other biometrictraits Vascular pattern technologies afford advantages suchas better usability and higher recognition accuracy

The rest of this paper is structured as follows Section 2presents the details on the survey of the proposed workSection 3 presents the details on preprocessing steps formultispectral palm print biometric system and the blockdiagram of the proposed multispectral palm print systemfor human identification Section 4 describes the proposedmultispectral palm print feature extraction methods andclassification approach for automated multispectral palmprint identification Experimental results from the proposedand prior approaches are discussed in Section 5 The keysolutions are summarized in Section 6

2 Related Work

There are many different features in multispectral palm printimage such as the delta point and the principle line and the

Hindawi Publishing Corporatione Scientific World JournalVolume 2015 Article ID 861629 11 pageshttpdxdoiorg1011552015861629

2 The Scientific World Journal

geometry furthermoremultispectral component show signif-icant textural features similar to the features of fingerprints asdiscussed by Lin et al [3] In [4ndash9] the researchers suggestedmany multispectral palm print methodologies Kumar andPrathyusha [10] suggested a new approach using triangula-tion of multispectral palm print images and extraction ofknuckle shape In [9ndash11] the researcher suggested variousmultimodal biometric authentications using multispectralpalmprintmethodology and combined palmprintwith othertraits In another approach Wang et al [12] proposed ahand dorsal vascular approach using partition local binarypattern and validated output using similarity measures Khanet al [13] proposed a vascular pattern recognition principalcomponent analysis which is a lower dimensional imagevascular pattern features

Multispectral palm print recognition methods can beclassified into two typesrsquo holistic approaches and geometricapproaches Holistic approaches use subspace learning Var-ious subspaces have been investigated for the multispectralpalm print recognition In [14] the researcher suggestedSIFT and LPP and (PCA) principal component analysis aresuggested by Wang et al [15] In this subspace learningapproaches subspace coefficients are generated which isused for recognition during matching The multispectralpalm print images represent vascular structure and thereforethe extraction of geometric feature has invited a lot ofresearch focus Here the ROI extraction is attained usingspatial domain filtering and a number of filters have beeninvestigated In [16] the researcher suggested SUSAN edgedetector cut-off Gaussian filter is suggested by Toh et al [17]Gabor filter is suggested by Zhang et al [18] and orthogonalGaussian filter is suggested by Hao et al [19] and Safa et al[20] proposed behavioral pattern based identification

The summary of previous work in the literature suggestedthat not so many researchers have paid attention to use mul-tiple algorithms to increase the recognition accuracy Andthe literature proposes the deficient of systematic compari-son of different feature representation for the multispectralpalm print methodologies in [16ndash18] the researcher usedconstrained images in their research and Hao et al [19] usecontact-free images

This paper proposes multiple algorithms based multi-spectral palm print recognitionThe proposed approach usestwo newmethodologies to extract two different multispectralfeatures and obtained improved recognition accuracy Theproposed subspace learning approach using KPCA (kernelprincipal component analysis) and MFRT (modified finiteradon transform) approach offers a computationally efficientandutilizesminimum templates size compared to the existingapproachesThe proposedmethod obtained improved recog-nition performance as compared to the previous multispec-tral palm print approaches presented in the literature

3 Proposed Work

A review of previous work on multispectral palm printrecognition methodologies presented in the earlier sectionsketches the importance of comparative performance on the

most promising multispectral palm print methodologies Inaddition the earlier efforts have been more concentrated onconstraint images rather than contact less images The keycontributions are as follows

(a) Here we used a comparative analysis of the pro-posedmethodologies in both constrained and contactless multispectral palm print imaging environmentwhich assures establishing the robustness of proposedmethod

(b) The proposed work thoroughly assesses the recog-nition performance for multispectral palm printrecognition The recognition performances are verygood even under the minimum number of trainingsamples (only one sample for each class) This paperalso explored the recognition performance of variousapproaches using two different databases to access theperformance using different approaches

The multispectral palm print images in peg-free imagingsystempresented havemany translational and rotational vari-ations Therefore accurate preprocessing steps are requiredto obtain an aligned and stable ROI The multispectralpalm print images are binarized to separate the palm regionfrom the background region Then distance from the centrelocation of the binarized palm image to the border of palmimage is calculated Due to the contactless environmentpotential scale changes can be quite large and with theintention of accounting for this variation it is sensible toadaptively choose the size and location of the ROIThis can bedone using certain image-specific procedures from the palmTo illustrate the multispectral palm print texture patternmore clearly image enhancement is required From Figure 1it is clear that the image normalization and enhancementenhanced the fine points and contrast of the ROI images

4 Methodology

The block diagram of the proposed methodology is shownin Figure 2 The acquired multispectral palm print imagesare first subjected to preprocessing steps which extractsthe region of interest (ROI) The enhanced and normalisedmultispectral palm print images are employed with mul-tiple feature extraction algorithms using kernel principalcomponent analysis (KPCA) and modified radon trans-form (MFRT) The features are fused using feature fusionmethodology and features are classified using LMKNCNclassification (Figure 5)Matching scores obtained are similarto conventional biometric system

The preprocessed multispectral palm print image repre-sents curved multispectral patterns and small line segmentswhich are rather curved can approximate these patternsTherefore in this research a new approach is recommendedto extract such features Here multiple feature extractionusing kernel principal component analysis (KPCA) andmodified finite radon transform (MFRT) is proposed Theproposed approach can effectively accommodate for morefrequent translational rotational variations and image dis-tortions

The Scientific World Journal 3

(a)

(b)

Figure 1 Image enhancement (a) original ROI image (b) enhanced image

ROI extraction

Preprocessing(vein region segmentation

image enhancement)

ROI extraction


image enhancement)

Image acquisition

Image acquisition

Feature extraction (KPCA)

Feature extraction (MFRT)

Feature fusion

LMNKNCNclassification Matching

Database

Decision

Figure 2 Block diagram for personal identification using multi-spectral palm print biometric system

41 Kernel Principal Component Analysis (KPCA) FeatureExtraction In principal component analysis firstly everyeigenvalue and eigenvector are computed and arrangedThen eigenvectors occurring at the topmost are selected toproject the input data Projecting the input into the selectedeigenvectors the size of the input data significantly decreasedInitially the mean centre of the image is computed as 11989810158401015840 isthe mean image as given in

11989810158401015840=

111987210158401015840

11987210158401015840

sum

119894=11199091 (1)

where 1199091 is a dimensional vector

Mean centred image (119898119895) is represented in

119898119895= 1199091 minus119898

10158401015840 (2)

Then covariance matrix is accomplished by

119862 = 1198721015840101584011987210158401015840119879 (3)

where 11987210158401015840 represents the composed matrix of the columnvectors119898

119895

Solving 1205821198991= 119862119899

1eigenvectors and eigenvalues are

obtained assuming 120582 is eigenvector and 1198991is eigenvalue

Consider

1198721015840101584011987210158401015840119879(11987210158401015840119899) = 120582

1015840(11987210158401015840119899) (4)

Itmeans that the first11987210158401015840minus1 eigenvectors (120582) and eigenvalues(1198991) could be attained by computing1198721015840101584011987210158401015840119879When 119872

10158401015840 eigenvalues and eigenvectors are accom-plished the images will be projected onto 119871 ≪ 119872

10158401015840

dimensions using

Ω10158401015840= [1198991 1198992 119899119871]

119879

(5)

where Ω10158401015840 correspond to the projected value To decidethe best picture of a multispectral palm print image theEuclidean distance can be computed using

12059810158401015840

119896=

10038171003817100381710038171003817Ω10158401015840minusΩ10158401015840

119896

10038171003817100381710038171003817 (6)

At the final step the minimum assigns the unidentifieddata into 119896 class KPCA is the same as PCA In KPCA the


minus4 minus3 minus2 minus1 0 1 2 3 4minus3

minus2

minus1

0

1

2

3Gaussian kernel PCA

(a)

minus09 minus08 minus07 minus06 minus05 minus04 minus03 minus02 minus01034

036

038

04

042

044

046Reconstructed preimages of Gaussian kPCA

(b)

Figure 3 Feature extraction (a) KPCA feature using Gaussian mapping (b) restructured image of (a)

input data is nonlinearly mapped and principal componentanalysis carried out on the obtained data The mapping isdenoted asΦ Figure 3 illustrates the feature extraction outputof KPCA After extracting the multiple feature extractionchoosing the suitable classifier plays a main role in gettinghigher accuracy For the proposed technique LMKNCNclassification techniques could be used for the enhancedresults

42 Modified Finite Radon Transform Feature ExtractionMethod Theradon transform is the integral transformwhichcontains the integral of a function over straight lines Other-wise it is the projection of an image along particular direc-tion [20ndash22] Let us assume that (1199091 1199101) are the Cartesiancoordinates of a point in a 2D image and 1198681(1199091 1199101) is theintensity The 2-dimensional radon transform is representedas 119877119868(120588 120579)

119877119868(120588 120579)

= int

+infin

minusinfin

int

+infin

minusinfin

1198681 (1199091 1199101) 120575 (120588 minus 119909 cos 120579 minus119910 sin 120579) 119889119909 119889119910(7)

where 120588 is the perpendicular distance of a line from the originand 120579 is the angle produced by the distance vector In theprojected scheme we have assumed 120579 to be varying from0∘ to180∘ To compute the eigenvalues of the attained radon imagegiven a matrix 119862 a scalar 120582

1is called an eigenvalue of 119862 if

there exists a nonzero vector 1199091 such that

1198621199091 = 1205821199091 (8)

Thus for a square matrix 119862 of size 119898 times 119898 the eigenvalueequation can be given as

1198621199091 minus1205821198681199091 = 0 (9)

where 119868 is the identitymatrix of size119898times119898 To get a nontrivialsolution for 1199091 determinant value is given as

det (119862 minus 120582119868) = 0 (10)

In the proposed methodology the angle of projection 120579is varying from 0∘ to 180∘ and radon transform of an image

is represented as a matrix As the ROI of multispectral palmprint image is of same size for all the individuals the radontransform attained for all the images would be of similarsize However the attained radon transform may not be asquare matrix In order to make a square matrix we induceequal number of extra rowscolumns with all zeroes to radonmatrix of each subject Finally eigenvalues are calculated forradon matrix Figure 4 illustrates the modified finite radontransform output of the cropped canny image

In the proposed multispectral palm print system thehand vascular features are the directional information of thehand vascular images The features are obtained by using theradon projection of a hand vascular image in different angleImage intensity is calculated for each projection vector Theprojection matrix is given as

119867 =

[

[

[

[

[

[

[

[

1199011205791(1) 119901

1205791(2) sdot sdot sdot 119901

1205791(119871)

1199011205792(1) 119901

1205792(2) sdot sdot sdot 119901

1205792(119871)

sdot sdot sdot

119901120579119896

(1) 119901120579119896

(2) sdot sdot sdot 119901120579119896

(119871)

]

]

]

]

]

]

]

]

(11)

where 119896 is the total projection angles and 119871 is the numberof projection points at 120579

119894(119894 = 1 2 119896) Here the method-

ologies for multialgorithm multispectral palm print systemfocus on the feature fusion Feature fusion is proposed since ituses feature sets that are closest to raw biometric data whichcontains rich information compared to matching score anddecision level fusionTherefore the current investigations ona feature fusion at the feature levels are expected to obtainbetter recognition accuracy compared to other level fusions

43 LocalMeans119870-Nearest Centroid Neighbour ClassificationNonparametric classifier KNN is effective simple and speedymethod to classify data especially in the case of small trainingsample size In this technique Euclidian distance is usedas for classifying To improve the performance of KNNvarious methodologies have been proposed out of whichnewest is LMKNCN The subsequent section explains themathematical explanation of LMKNCN

Let 119904 = 119909119899isin 119877119898119873

119899=1 be a test set with119872 classes 1198881 119888119872and 119904119894= 119909119894119895isin 119877119898119873

119895=1 be a class test set of 119888119894 each consist of


(a)

minus200

minus150

minus100

minus50

0

50

100

150

200

0 50 100 150

120579 (deg)

Modified finite radontransform of test image

1

08

06

04

02

0

120588

(b)

Figure 4 (a) Cropped and canny image and (b) MFRAT feature extraction

004

003

002

001

0

minus001

minus002

minus003

minus004minus001 0 001 002 003 004

Classification using LMKNCN

Figure 5 Classification result using LMKNCN

training samples 119873119894 In the proposed methodology when a

query pattern is given its class label can be calculated usingthe following sequences

(i) For a given query pattern the set of KNCN using theset 119904119894of each class 119888

119894can be calculated using

119904NCN119894119896

= 119877NCN119894

(119909) (12)

(ii) Calculate the local centric mean vector 119881NCN119894119896

fromeach 119888

119894using 119904NCN

119894119896

119881NCN119894119896

=

1119896

119896

sum

119895=1119909NCN119894119895

(13)

(iii) Compute the distance 119889(119909 119881NCN119894119896

) between 119909 and119881

NCN119894119896

(iv) To obtain the closest distance between local mean

vector and query pattern calculate

119879 = argmin 119889 (119909 119881NCN119894119896

) (14)

5 Experimental ClassificationResults and Analysis

In order to assess the effectiveness of the proposed methodfor multispectral palm print identification The researcherevaluated performance using thorough experiments on bothcontact and contact less based databases The extractionof multispectral palm print features using Laplacian palm[10] ordinal code [18] and scale invariant feature transform(SIFT) [13] has been investigated in the literature withimproved results Here the investigation compared with allthe previous methodologies mentioned in the literature withour proposed methodology The performance of the pro-posedmultialgorithmmultispectral palm print methodologyis verified using two databases (ie) CASIA [23] (imagesacquired under 850 nm wavelength) and Poly U [24] (nearinfrared images) database

In this experiment for the comparative evaluation ofthe multispectral biometrics authentication using minimumnumber of training samples per class is considered Thisresearch evaluated the effectiveness of the proposed method-ology for various training samples size This research under-goes three experiments on each of the two databases firstlythe recognition performance from the personals (right andleft hands) multispectral palm print images was evaluatedSecondly to assure the performance researcher combinedright hand and left hand multispectral palm print images areconsidered The average performance recognition accuracyfor various gallery samples fromCASIAdatabase is illustrated


085

09

095

1

Gen

uine

acce

ptan

ce ra

teAverage performance CASIA (left)

1 MFRTKPCA2 MFRT3 KPCA

4 OC5 LP1 CC

False acceptance rate10minus3 10minus2 10minus1 100

(a)


4 OC5 LP1 CC

086

088

09

092

094

096

098

1

False acceptance rate

Gen

uine

acce

ptan

ce ra

te

Average performance CASIA (right)

10minus3 10minus2 10minus1 100

(b)

082084086088

09092094096098

1

Gen

uine

acce

ptan

ce ra

te

Average performance CASIA (right and left)


4 OC5 LP1 CC


(c)

Figure 6 Receiver operating characteristics from the CASIA database (a) left palm (b) right palm and (c) left and right palm

Table 1 Accuracy measures from CASIA left hand multispectral palm print image

Number of samplesper class MFRT + KPCA MFRT KPCA Ordinal code Laplacian code Comp code SIFT

1 9431 9412 9415 9100 7500 8467 65332 9822 9633 9744 9633 8333 9300 73003 9836 9846 9822 9767 8700 9533 77674 9982 9871 9882 9878 8750 9575 7788

in Tables 1ndash3 It can be observed from Figure 6 that theproposed multialgorithm (KPCA + MFRT) multispectralpalm print approach outperforms other promising methodsin all three experiments especially in the case of a minimumnumber of samples per class Unlike the other matchingapproaches the proposed method accommodates higherdeformation and image variations

Recognition performances from the Poly Umultispectralpalm print database are illustrated in Tables 4ndash6 The per-formance characteristics are obtained from Figure 7 Fromthe above observation the proposed methodology of multi-algorithm multispectral palm print consistently outperforms

other promising approaches in all three experiments even inthe case of a smaller number of train images for each classUnlike other methodologies the proposed method accom-modates higher deformation and image variation and pro-duces best matching Tables 7 and 8 suggest the performanceimprovement for the various methodologies with increasingnumber of samples per class From the observation it issuggested that the multialgorithm multispectral approachachieves 023 EER for four samples per class using CASIAdatabase In addition the same approach achieves 0006EER for six samples per class using Poly Umultispectral palmprint database


086088

09092094096098

1

Gen

uine

acce

ptan

ce ra

te

Average performance Poly U (left)


4 OC5 LP1 CC

False acceptance rate10minus410minus5 10minus3 10minus2 10minus1 100

(a)

088

09

092

094

096

098

1

Gen

uine

acce

ptan

ce ra

te

Average performance Poly U (right)


4 OC5 LP1 CC


(b)


4 OC5 LP1 CC

085

09

095

1

Gen

uine

acce

ptan

ce ra

te

Average performance Poly U (right and left)


(c)

Figure 7 Receiver operating characteristics from the Poly U multispectral palm print database (a) left palm (b) right palm and (c) left andright palm

Table 2 Accuracy measures from CASIA right hand multispectral palm print image


1 9537 9625 9545 9333 7533 8783 68002 9832 9738 9752 9517 8300 9367 75673 9846 9840 9862 9783 8783 9617 80834 9981 9890 9882 9891 8896 9783 8345

Table 3 Accuracy measures from CASIA right and left hand multispectral palm print image


1 9737 9725 9645 9433 8033 8483 76062 9845 9738 9752 9717 8100 9369 78603 9866 9868 9872 9883 8287 9517 79634 9988 9888 9879 9863 8300 9530 8000


Table 4 Accuracy measures from Poly U left hand multispectral palm print image

Number of Samplesper class MFRT + KPCA MFRT KPCA Ordinal code Laplacian code Comp code SIFT

1 9941 9748 9838 9100 7767 8580 75842 9962 9943 9953 9500 8513 9142 78083 9970 9958 9972 9600 8740 9293 78334 9982 9961 9982 9730 8880 9435 78635 9999 9981 9996 9793 8933 9580 79046 100 9989 9999 9893 8921 9687 7928

Table 5 Accuracy measures from Poly U right hand multispectral palm print image


1 9956 9848 9945 9133 7613 8596 64042 9966 9949 9960 9550 8579 9142 70383 9972 9952 9967 9682 8796 9271 72634 9989 9965 9985 9726 8892 9329 74135 9999 9981 9999 9786 8913 9467 75046 100 9995 9999 9886 8892 9500 7558

Table 6 Accuracy measures from Poly U left and right hand multispectral palm print image


1 9958 9842 9945 9133 6413 8596 74042 9970 9948 9958 9350 7679 9242 70383 9987 9962 9980 9482 8396 9311 72634 9997 9966 9988 9586 8792 9329 74135 9999 9991 9992 9586 8813 9407 75046 100 9996 9997 9786 8892 9450 7558

Table 7 Equal error rate accuracy measures from CASIA palm print database


1 139 148 162 311 1089 601 11552 068 087 087 161 793 311 6993 054 062 058 100 479 200 5194 023 041 038 198 400 198 400

Table 8 Equal error rate accuracy measures from Poly U palm print database


1 032 068 062 143 1093 448 5582 008 047 047 118 643 274 3313 004 032 028 113 413 180 2244 003 021 018 081 370 138 1945 001 018 010 078 315 111 1686 0006 010 008 067 256 095 158


Table 9 Comparison of multispectral palm print approaches

Methodology Classification Pegs Size EERGabor featureshand veinKumar and Prathyusha [22]

Euclidean distance No [CASIA] 106 2740

Multispectraldorsal vein imageWang et al [15]

Support vector machine Yes [Poly U] 250 0012

Gabor featurespalm printZhang et al [18]

Ant colony optimiation No [CASIA] 100 3120

LBPpalm veinKang and Wu [25]

Histogram matching No [CASIA] 100 0267

Root sift featurespalm veinKang et al [26]

LBP based mismatchingremoval No [CASIA] 105 0996

Log gaborpalm veinRaghavendra and Busch [27]

Spare representation No [CASIA] 105 840

Gabor featurepalm veinKisku et al [24]

Support vector machine No [CASIA] 100 3125

Proposed(KPCA + MFRT) LMKNCN No [CASIA] 200 0230

Proposed(KPCA + MFRT) LMKNCN Yes [Poly U] 200 0006

0

1

2

3

4

5

6

Gen

uine

acce

ptan

ce ra

te

Equal error rate (CASIA)


4 OC5 LP1 CC


(a)


4 OC5 LP1 CC

005

115

225

335

4

Gen

uine

acce

ptan

ce ra

te

Equal error rate (Poly U)


(b)

Figure 8 Equal error rate ROC plot (a) CASIA (b) Poly U

Table 9 summarizes the multispectral palm printapproaches reviewed in the literature and suggested thesuperior recognition performance in the proposed approachThe performance improvement increases with increasein number of training samples on both CASIA and PolyU database Comparatively improved performance wasobtained even when the minimum number of trainingsamples is considered (only one sample for each class)

The experimental results presented here consistentlysuggest that the proposedmultialgorithmmultispectral palmprint recognition approach using KPCA and MFRT achievessignificantly improved performance over the previously pro-posed approaches on both peg and non-peg multispectralpalm print databases In order to comparatively determinethe performance form the two databases corresponding ROCare shown in Figure 8 It can be observed from the figure that


the proposed methodology performs consistently better thanthe other methods

6 Conclusion

In this paper we have investigated a novel approach forhuman identification using multispectral palm print imagesto represent a low resolution multispectral palm print imageand to match different multispectral palm print imagewe proposed the kernel principal component analysis andmodified radon transform methods We proposed methodcan effectively accommodate the rotational variation trans-lational variation and potential image deformation We pre-sented an automatic multispectral palm print identificationwhich can more reliably extract the multispectral palm printfeatures and achieve much higher accuracy than previouslyproposedmultispectral palm print identificationmethodolo-gies Our proposed multialgorithm based multispectral palmprint identification approachworksmore effectively and leadsto a more accurate performance even with the minimumnumber of enrolled images (one sample for trainee) Wepresented rigorous experimental results and compared tothe existing methods using two different databases CASIAand Poly U palm print Here we obtained identification rateof 9866 9988 9999 and 100 from the left rightmultispectral palm print images of the CASIA and Poly Udatabases respectively and correspondingly obtained EER of054 023 001 and 0006 Performance improvement can befurther increased by fusing multispectral palm print imagewith other suitable traits Further a more efficient featureextraction algorithm and classification techniques should bedeveloped to deal with shadow and undesired noise andto decrease the matching score for illegal users and raisethe matching score for legal users Moreover it should alsobe competent to reduce the effects from the orientationnonrigid deformations and translations of the multispectralpalm print image

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] A K Jain and J Feng ldquoLatent palmprint matchingrdquo IEEETransactions on Pattern Analysis and Machine Intelligence vol31 no 6 pp 1032ndash1047 2009

[2] PMacGregor and RWelford ldquoVein check imaging for securityand personnel identificationrdquoAdvance Imaging vol 6 no 7 pp52ndash56 1991

[3] X Lin B Zhuang X Su Y Zhou and G Bao ldquoMeasurementand matching of human vein pattern characteristicsrdquo Journal ofTsinghua University vol 43 no 2 pp 164ndash167 2003

[4] Z L Wang B C Zhang W P Chen and Y S Gao ldquoAperformance evaluation of shape and texture basedmethods forvein recognitionrdquo inProceedings of the 1st International Congresson Image and Signal Processing (CISPrsquo 08) vol 2 pp 659ndash661May 2008

[5] A M Al-Juboori W Bu X Wu and Q Zhao ldquoPalm veinverification using multiple features and locality preservingprojectionsrdquo The Scientific World Journal vol 2014 Article ID246083 11 pages 2014

[6] C-L Lin and K-C Fan ldquoBiometric verification using thermalimages of palm-dorsa vein patternsrdquo IEEE Transactions onCircuits and Systems for Video Technology vol 14 no 2 pp 199ndash213 2004

[7] R Gayathri and P Ramamoorthy ldquoAutomatic palmprint iden-tification based on high order Zernike momentrdquo AmericanJournal of Applied Sciences vol 9 no 5 pp 759ndash765 2012

[8] R Gayathri and P Ramamoorthy ldquoPalmprint recognition usingfeature level fusionrdquo Journal of Computer Science vol 8 no 7pp 1049ndash1061 2012

[9] LWang G Leedham andD Siu-Yeung Cho ldquoMinutiae featureanalysis for infrared hand vein pattern biometricsrdquo PatternRecognition vol 41 no 3 pp 920ndash929 2008

[10] A Kumar and K V Prathyusha ldquoPersonal authentication usinghand vein triangulation and knuckle shaperdquo IEEE Transactionson Image Processing vol 18 no 9 pp 2127ndash2136 2009

[11] J G Wang W Y Yau and A Suwandy ldquoFusion of palmprintand palm vein images for person recognition based on laplacianpalm featurerdquo Pattern Recognition vol 41 no 5 pp 1514ndash15272008

[12] Y Wang K Li J Cui L-K Shark and M Varley ldquoStudy ofhand-dorsa vein recognitionrdquo in Advanced Intelligent Comput-ing Theories and Applications Proceedings of the 6th Interna-tional Conference on Intelligent Computing ICIC 2010 Chang-sha China August 18ndash21 2010 vol 6215 of Lecture Notes inComputer Science pp 490ndash498 Springer Berlin Germany2010

[13] M M Khan R K Subramanian and N A Khan ldquoLow dimen-sional representation of dorsal hand vein features using prin-ciple component analysis (PCA)rdquo World Academy of ScienceEngineering and Technology vol 49 pp 1001ndash1007 2009

[14] P-O Ladoux C Rosenberger and B Dorizzi ldquoPalm veinverification system based on SIFT matchingrdquo in Advances inBiometrics vol 5558 of Lecture Notes in Computer Science pp1290ndash1298 Springer Berlin Germany 2009

[15] J-G Wang W-Y Yau A Suwandy and E Sung ldquoPersonrecognition by fusing palmprint and palm vein images basedon lsquoLaplacianpalmrsquo representationrdquo Pattern Recognition vol 41no 5 pp 1514ndash1527 2008

[16] J-G Wang W-Y Yau and A Suwandy ldquoFeature-level fusionof palmprint and palm vein for person identification based ona lsquojunction pointrsquo representationrdquo in Proceedings of the IEEEInternational Conference on Image Processing (ICIP rsquo08) pp253ndash256 IEEE San Diego Calif USA October 2008

[17] K-A Toh H-L Eng Y-S Choo Y-L Cha W-Y Yau andK-S Low ldquoIdentity verification through palm vein and creasetexturerdquo in Advances in Biometrics vol 3832 of Lecture Notesin Computer Science pp 546ndash553 Springer Berlin Germany2005

[18] D Zhang Z Guo G Lu L Zhang andW Zuo ldquoAn online sys-tem of multispectral palmprint verificationrdquo IEEE Transactionson Instrumentation and Measurement vol 59 no 2 pp 480ndash490 2010

[19] Y Hao Z Sun T Tan and C Ren ldquoMultispectral palmimage fusion for accurate contact-free palmprint recognitionrdquoin Proceedings of the IEEE International Conference on ImageProcessing (ICIP rsquo08) pp 281ndash284 October 2008


[20] N S Safa N Abdul Ghani and M A Ismail ldquoAn artificialneural network classification approach for improving accuracyof customer identification in e-commercerdquo Malaysian Journalof Computer Science vol 27 no 3 pp 171ndash185 2014

[21] Y-B Zhang Q Li J You and P Bhattacharya ldquoPalmvein extraction and matching for personal authenticationrdquo inAdvances in Visual Information Systems vol 4781 of LectureNotes in Computer Science pp 154ndash164 Springer Berlin Ger-many 2007


[23] httpwww4comppolyueduhksimbiometricsMultispectral-PalmprintMSPhtm

[24] D R Kisku A Rattani P Gupta J K Sing and C J HwangldquoHuman identity verification using multispectral palmprintfusion rdquo Journal of Signal and Information Processing vol 3 no2 pp 263ndash273 2012

[25] W Kang and Q Wu ldquoContactless palm vein recognitionusing a mutual foreground-based local binary patternrdquo IEEETransactions on Information Forensics and Security vol 9 no11 pp 1974ndash1985 2014

[26] W Kang Y Liu Q Wu and X Yue ldquoContact-free palm-veinrecognition based on local invariant featuresrdquo PLoS ONE vol9 no 5 Article ID e97548 2014

[27] R Raghavendra and C Busch ldquoNovel image fusion schemebased on dependency measure for robust multispectral palm-print recognitionrdquo Pattern Recognition vol 47 no 6 pp 2205ndash2221 2014

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Distributed Sensor Networks


Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014


Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia


Biomedical Imaging


ArtificialNeural Systems

Advances in


RoboticsJournal of



Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in



geometry furthermoremultispectral component show signif-icant textural features similar to the features of fingerprints asdiscussed by Lin et al [3] In [4ndash9] the researchers suggestedmany multispectral palm print methodologies Kumar andPrathyusha [10] suggested a new approach using triangula-tion of multispectral palm print images and extraction ofknuckle shape In [9ndash11] the researcher suggested variousmultimodal biometric authentications using multispectralpalmprintmethodology and combined palmprintwith othertraits In another approach Wang et al [12] proposed ahand dorsal vascular approach using partition local binarypattern and validated output using similarity measures Khanet al [13] proposed a vascular pattern recognition principalcomponent analysis which is a lower dimensional imagevascular pattern features

Multispectral palm print recognition methods can beclassified into two typesrsquo holistic approaches and geometricapproaches Holistic approaches use subspace learning Var-ious subspaces have been investigated for the multispectralpalm print recognition In [14] the researcher suggestedSIFT and LPP and (PCA) principal component analysis aresuggested by Wang et al [15] In this subspace learningapproaches subspace coefficients are generated which isused for recognition during matching The multispectralpalm print images represent vascular structure and thereforethe extraction of geometric feature has invited a lot ofresearch focus Here the ROI extraction is attained usingspatial domain filtering and a number of filters have beeninvestigated In [16] the researcher suggested SUSAN edgedetector cut-off Gaussian filter is suggested by Toh et al [17]Gabor filter is suggested by Zhang et al [18] and orthogonalGaussian filter is suggested by Hao et al [19] and Safa et al[20] proposed behavioral pattern based identification

The summary of previous work in the literature suggestedthat not so many researchers have paid attention to use mul-tiple algorithms to increase the recognition accuracy Andthe literature proposes the deficient of systematic compari-son of different feature representation for the multispectralpalm print methodologies in [16ndash18] the researcher usedconstrained images in their research and Hao et al [19] usecontact-free images

This paper proposes multiple algorithms based multi-spectral palm print recognitionThe proposed approach usestwo newmethodologies to extract two different multispectralfeatures and obtained improved recognition accuracy Theproposed subspace learning approach using KPCA (kernelprincipal component analysis) and MFRT (modified finiteradon transform) approach offers a computationally efficientandutilizesminimum templates size compared to the existingapproachesThe proposedmethod obtained improved recog-nition performance as compared to the previous multispec-tral palm print approaches presented in the literature

3 Proposed Work

A review of previous work on multispectral palm printrecognition methodologies presented in the earlier sectionsketches the importance of comparative performance on the

most promising multispectral palm print methodologies Inaddition the earlier efforts have been more concentrated onconstraint images rather than contact less images The keycontributions are as follows

(a) Here we used a comparative analysis of the pro-posedmethodologies in both constrained and contactless multispectral palm print imaging environmentwhich assures establishing the robustness of proposedmethod

(b) The proposed work thoroughly assesses the recog-nition performance for multispectral palm printrecognition The recognition performances are verygood even under the minimum number of trainingsamples (only one sample for each class) This paperalso explored the recognition performance of variousapproaches using two different databases to access theperformance using different approaches

The multispectral palm print images in peg-free imagingsystempresented havemany translational and rotational vari-ations Therefore accurate preprocessing steps are requiredto obtain an aligned and stable ROI The multispectralpalm print images are binarized to separate the palm regionfrom the background region Then distance from the centrelocation of the binarized palm image to the border of palmimage is calculated Due to the contactless environmentpotential scale changes can be quite large and with theintention of accounting for this variation it is sensible toadaptively choose the size and location of the ROIThis can bedone using certain image-specific procedures from the palmTo illustrate the multispectral palm print texture patternmore clearly image enhancement is required From Figure 1it is clear that the image normalization and enhancementenhanced the fine points and contrast of the ROI images

4 Methodology

The block diagram of the proposed methodology is shownin Figure 2 The acquired multispectral palm print imagesare first subjected to preprocessing steps which extractsthe region of interest (ROI) The enhanced and normalisedmultispectral palm print images are employed with mul-tiple feature extraction algorithms using kernel principalcomponent analysis (KPCA) and modified radon trans-form (MFRT) The features are fused using feature fusionmethodology and features are classified using LMKNCNclassification (Figure 5)Matching scores obtained are similarto conventional biometric system

The preprocessed multispectral palm print image repre-sents curved multispectral patterns and small line segmentswhich are rather curved can approximate these patternsTherefore in this research a new approach is recommendedto extract such features Here multiple feature extractionusing kernel principal component analysis (KPCA) andmodified finite radon transform (MFRT) is proposed Theproposed approach can effectively accommodate for morefrequent translational rotational variations and image dis-tortions


(a)

(b)


ROI extraction


image enhancement)

ROI extraction


image enhancement)

Image acquisition

Image acquisition



Feature fusion


Database

Decision



11989810158401015840=

111987210158401015840

11987210158401015840

sum

119894=11199091 (1)



119898119895= 1199091 minus119898

10158401015840 (2)


119862 = 1198721015840101584011987210158401015840119879 (3)


119895

Solving 1205821198991= 119862119899



Consider

1198721015840101584011987210158401015840119879(11987210158401015840119899) = 120582

1015840(11987210158401015840119899) (4)



10158401015840

dimensions using

Ω10158401015840= [1198991 1198992 119899119871]

119879

(5)


12059810158401015840

119896=

10038171003817100381710038171003817Ω10158401015840minusΩ10158401015840

119896

10038171003817100381710038171003817 (6)




minus2

minus1

0

1

2


(a)


036

038

04

042

044


(b)




119877119868(120588 120579)

= int

+infin

minusinfin

int

+infin

minusinfin





1198621199091 = 1205821199091 (8)


1198621199091 minus1205821198681199091 = 0 (9)


det (119862 minus 120582119868) = 0 (10)




119867 =

[

[

[

[

[

[

[

[

1199011205791(1) 119901

1205791(2) sdot sdot sdot 119901

1205791(119871)

1199011205792(1) 119901

1205792(2) sdot sdot sdot 119901

1205792(119871)

sdot sdot sdot

119901120579119896

(1) 119901120579119896

(2) sdot sdot sdot 119901120579119896

(119871)

]

]

]

]

]

]

]

]

(11)


119894(119894 = 1 2 119896) Here the method-



Let 119904 = 119909119899isin 119877119898119873




(a)

minus200

minus150

minus100

minus50

0

50

100

150

200

0 50 100 150

120579 (deg)


1

08

06

04

02

0

120588

(b)


004

003

002

001

0

minus001

minus002

minus003

minus004minus001 0 001 002 003 004







119904NCN119894119896

= 119877NCN119894

(119909) (12)


fromeach 119888

119894using 119904NCN

119894119896

119881NCN119894119896

=

1119896

119896

sum

119895=1119909NCN119894119895

(13)


) between 119909 and119881

NCN119894119896



119879 = argmin 119889 (119909 119881NCN119894119896

) (14)





085

09

095

1

Gen

uine

acce

ptan

ce ra



4 OC5 LP1 CC


(a)


4 OC5 LP1 CC

086

088

09

092

094

096

098

1


Gen

uine

acce

ptan

ce ra

te



(b)

082084086088

09092094096098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(c)




1 9431 9412 9415 9100 7500 8467 65332 9822 9633 9744 9633 8333 9300 73003 9836 9846 9822 9767 8700 9533 77674 9982 9871 9882 9878 8750 9575 7788





086088

09092094096098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(a)

088

09

092

094

096

098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(b)


4 OC5 LP1 CC

085

09

095

1

Gen

uine

acce

ptan

ce ra

te



(c)




1 9537 9625 9545 9333 7533 8783 68002 9832 9738 9752 9517 8300 9367 75673 9846 9840 9862 9783 8783 9617 80834 9981 9890 9882 9891 8896 9783 8345



1 9737 9725 9645 9433 8033 8483 76062 9845 9738 9752 9717 8100 9369 78603 9866 9868 9872 9883 8287 9517 79634 9988 9888 9879 9863 8300 9530 8000




1 9941 9748 9838 9100 7767 8580 75842 9962 9943 9953 9500 8513 9142 78083 9970 9958 9972 9600 8740 9293 78334 9982 9961 9982 9730 8880 9435 78635 9999 9981 9996 9793 8933 9580 79046 100 9989 9999 9893 8921 9687 7928



1 9956 9848 9945 9133 7613 8596 64042 9966 9949 9960 9550 8579 9142 70383 9972 9952 9967 9682 8796 9271 72634 9989 9965 9985 9726 8892 9329 74135 9999 9981 9999 9786 8913 9467 75046 100 9995 9999 9886 8892 9500 7558



1 9958 9842 9945 9133 6413 8596 74042 9970 9948 9958 9350 7679 9242 70383 9987 9962 9980 9482 8396 9311 72634 9997 9966 9988 9586 8792 9329 74135 9999 9991 9992 9586 8813 9407 75046 100 9996 9997 9786 8892 9450 7558



1 139 148 162 311 1089 601 11552 068 087 087 161 793 311 6993 054 062 058 100 479 200 5194 023 041 038 198 400 198 400



1 032 068 062 143 1093 448 5582 008 047 047 118 643 274 3313 004 032 028 113 413 180 2244 003 021 018 081 370 138 1945 001 018 010 078 315 111 1686 0006 010 008 067 256 095 158



















0

1

2

3

4

5

6

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(a)


4 OC5 LP1 CC

005

115

225

335

4

Gen

uine

acce

ptan

ce ra

te



(b)






6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




(a)

(b)


ROI extraction


image enhancement)

ROI extraction


image enhancement)

Image acquisition

Image acquisition



Feature fusion


Database

Decision



11989810158401015840=

111987210158401015840

11987210158401015840

sum

119894=11199091 (1)



119898119895= 1199091 minus119898

10158401015840 (2)


119862 = 1198721015840101584011987210158401015840119879 (3)


119895

Solving 1205821198991= 119862119899



Consider

1198721015840101584011987210158401015840119879(11987210158401015840119899) = 120582

1015840(11987210158401015840119899) (4)



10158401015840

dimensions using

Ω10158401015840= [1198991 1198992 119899119871]

119879

(5)


12059810158401015840

119896=

10038171003817100381710038171003817Ω10158401015840minusΩ10158401015840

119896

10038171003817100381710038171003817 (6)




minus2

minus1

0

1

2


(a)


036

038

04

042

044


(b)




119877119868(120588 120579)

= int

+infin

minusinfin

int

+infin

minusinfin





1198621199091 = 1205821199091 (8)


1198621199091 minus1205821198681199091 = 0 (9)


det (119862 minus 120582119868) = 0 (10)




119867 =

[

[

[

[

[

[

[

[

1199011205791(1) 119901

1205791(2) sdot sdot sdot 119901

1205791(119871)

1199011205792(1) 119901

1205792(2) sdot sdot sdot 119901

1205792(119871)

sdot sdot sdot

119901120579119896

(1) 119901120579119896

(2) sdot sdot sdot 119901120579119896

(119871)

]

]

]

]

]

]

]

]

(11)


119894(119894 = 1 2 119896) Here the method-



Let 119904 = 119909119899isin 119877119898119873




(a)

minus200

minus150

minus100

minus50

0

50

100

150

200

0 50 100 150

120579 (deg)


1

08

06

04

02

0

120588

(b)


004

003

002

001

0

minus001

minus002

minus003

minus004minus001 0 001 002 003 004







119904NCN119894119896

= 119877NCN119894

(119909) (12)


fromeach 119888

119894using 119904NCN

119894119896

119881NCN119894119896

=

1119896

119896

sum

119895=1119909NCN119894119895

(13)


) between 119909 and119881

NCN119894119896



119879 = argmin 119889 (119909 119881NCN119894119896

) (14)





085

09

095

1

Gen

uine

acce

ptan

ce ra



4 OC5 LP1 CC


(a)


4 OC5 LP1 CC

086

088

09

092

094

096

098

1


Gen

uine

acce

ptan

ce ra

te



(b)

082084086088

09092094096098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(c)




1 9431 9412 9415 9100 7500 8467 65332 9822 9633 9744 9633 8333 9300 73003 9836 9846 9822 9767 8700 9533 77674 9982 9871 9882 9878 8750 9575 7788





086088

09092094096098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(a)

088

09

092

094

096

098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(b)


4 OC5 LP1 CC

085

09

095

1

Gen

uine

acce

ptan

ce ra

te



(c)




1 9537 9625 9545 9333 7533 8783 68002 9832 9738 9752 9517 8300 9367 75673 9846 9840 9862 9783 8783 9617 80834 9981 9890 9882 9891 8896 9783 8345



1 9737 9725 9645 9433 8033 8483 76062 9845 9738 9752 9717 8100 9369 78603 9866 9868 9872 9883 8287 9517 79634 9988 9888 9879 9863 8300 9530 8000




1 9941 9748 9838 9100 7767 8580 75842 9962 9943 9953 9500 8513 9142 78083 9970 9958 9972 9600 8740 9293 78334 9982 9961 9982 9730 8880 9435 78635 9999 9981 9996 9793 8933 9580 79046 100 9989 9999 9893 8921 9687 7928



1 9956 9848 9945 9133 7613 8596 64042 9966 9949 9960 9550 8579 9142 70383 9972 9952 9967 9682 8796 9271 72634 9989 9965 9985 9726 8892 9329 74135 9999 9981 9999 9786 8913 9467 75046 100 9995 9999 9886 8892 9500 7558



1 9958 9842 9945 9133 6413 8596 74042 9970 9948 9958 9350 7679 9242 70383 9987 9962 9980 9482 8396 9311 72634 9997 9966 9988 9586 8792 9329 74135 9999 9991 9992 9586 8813 9407 75046 100 9996 9997 9786 8892 9450 7558



1 139 148 162 311 1089 601 11552 068 087 087 161 793 311 6993 054 062 058 100 479 200 5194 023 041 038 198 400 198 400



1 032 068 062 143 1093 448 5582 008 047 047 118 643 274 3313 004 032 028 113 413 180 2244 003 021 018 081 370 138 1945 001 018 010 078 315 111 1686 0006 010 008 067 256 095 158



















0

1

2

3

4

5

6

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(a)


4 OC5 LP1 CC

005

115

225

335

4

Gen

uine

acce

ptan

ce ra

te



(b)






6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





minus2

minus1

0

1

2


(a)


036

038

04

042

044


(b)




119877119868(120588 120579)

= int

+infin

minusinfin

int

+infin

minusinfin





1198621199091 = 1205821199091 (8)


1198621199091 minus1205821198681199091 = 0 (9)


det (119862 minus 120582119868) = 0 (10)




119867 =

[

[

[

[

[

[

[

[

1199011205791(1) 119901

1205791(2) sdot sdot sdot 119901

1205791(119871)

1199011205792(1) 119901

1205792(2) sdot sdot sdot 119901

1205792(119871)

sdot sdot sdot

119901120579119896

(1) 119901120579119896

(2) sdot sdot sdot 119901120579119896

(119871)

]

]

]

]

]

]

]

]

(11)


119894(119894 = 1 2 119896) Here the method-



Let 119904 = 119909119899isin 119877119898119873




(a)

minus200

minus150

minus100

minus50

0

50

100

150

200

0 50 100 150

120579 (deg)


1

08

06

04

02

0

120588

(b)


004

003

002

001

0

minus001

minus002

minus003

minus004minus001 0 001 002 003 004







119904NCN119894119896

= 119877NCN119894

(119909) (12)


fromeach 119888

119894using 119904NCN

119894119896

119881NCN119894119896

=

1119896

119896

sum

119895=1119909NCN119894119895

(13)


) between 119909 and119881

NCN119894119896



119879 = argmin 119889 (119909 119881NCN119894119896

) (14)





085

09

095

1

Gen

uine

acce

ptan

ce ra



4 OC5 LP1 CC


(a)


4 OC5 LP1 CC

086

088

09

092

094

096

098

1


Gen

uine

acce

ptan

ce ra

te



(b)

082084086088

09092094096098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(c)




1 9431 9412 9415 9100 7500 8467 65332 9822 9633 9744 9633 8333 9300 73003 9836 9846 9822 9767 8700 9533 77674 9982 9871 9882 9878 8750 9575 7788





086088

09092094096098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(a)

088

09

092

094

096

098

1

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(b)


4 OC5 LP1 CC

085

09

095

1

Gen

uine

acce

ptan

ce ra

te



(c)




1 9537 9625 9545 9333 7533 8783 68002 9832 9738 9752 9517 8300 9367 75673 9846 9840 9862 9783 8783 9617 80834 9981 9890 9882 9891 8896 9783 8345



1 9737 9725 9645 9433 8033 8483 76062 9845 9738 9752 9717 8100 9369 78603 9866 9868 9872 9883 8287 9517 79634 9988 9888 9879 9863 8300 9530 8000




1 9941 9748 9838 9100 7767 8580 75842 9962 9943 9953 9500 8513 9142 78083 9970 9958 9972 9600 8740 9293 78334 9982 9961 9982 9730 8880 9435 78635 9999 9981 9996 9793 8933 9580 79046 100 9989 9999 9893 8921 9687 7928



1 9956 9848 9945 9133 7613 8596 64042 9966 9949 9960 9550 8579 9142 70383 9972 9952 9967 9682 8796 9271 72634 9989 9965 9985 9726 8892 9329 74135 9999 9981 9999 9786 8913 9467 75046 100 9995 9999 9886 8892 9500 7558



1 9958 9842 9945 9133 6413 8596 74042 9970 9948 9958 9350 7679 9242 70383 9987 9962 9980 9482 8396 9311 72634 9997 9966 9988 9586 8792 9329 74135 9999 9991 9992 9586 8813 9407 75046 100 9996 9997 9786 8892 9450 7558



1 139 148 162 311 1089 601 11552 068 087 087 161 793 311 6993 054 062 058 100 479 200 5194 023 041 038 198 400 198 400



1 032 068 062 143 1093 448 5582 008 047 047 118 643 274 3313 004 032 028 113 413 180 2244 003 021 018 081 370 138 1945 001 018 010 078 315 111 1686 0006 010 008 067 256 095 158



















0

1

2

3

4

5

6

Gen

uine

acce

ptan

ce ra

te



4 OC5 LP1 CC


(a)


4 OC5 LP1 CC

005

115

225

335

4

Gen

uine

acce

ptan

ce ra

te



(b)






6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




(a)

minus200

minus150

minus100

minus50

0

50

100

150

200

0 50 100 150

120579 (deg)


1

08

06

04

02

0

120588

(b)


004

003

002

001

0

minus001

minus002

minus003

minus004minus001 0 001 002 003 004







119904NCN119894119896

= 119877NCN119894

(119909) (12)


fromeach 119888

119894using 119904NCN

119894119896

119881NCN119894119896

=

1119896

119896

sum

119895=1119909NCN119894119895

(13)


) between 119909 and119881

NCN119894119896



119879 = argmin 119889 (119909 119881NCN119894119896

) (14)





085

09

095

1

Gen

uine

acce

ptan

ce ra



4 OC5 LP1 CC


(a)


4 OC5 LP1 CC

086

088

09

092

094

096

098

1


Gen

uine

acce

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1 9431 9412 9415 9100 7500 8467 65332 9822 9633 9744 9633 8333 9300 73003 9836 9846 9822 9767 8700 9533 77674 9982 9871 9882 9878 8750 9575 7788





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1 9537 9625 9545 9333 7533 8783 68002 9832 9738 9752 9517 8300 9367 75673 9846 9840 9862 9783 8783 9617 80834 9981 9890 9882 9891 8896 9783 8345



1 9737 9725 9645 9433 8033 8483 76062 9845 9738 9752 9717 8100 9369 78603 9866 9868 9872 9883 8287 9517 79634 9988 9888 9879 9863 8300 9530 8000




1 9941 9748 9838 9100 7767 8580 75842 9962 9943 9953 9500 8513 9142 78083 9970 9958 9972 9600 8740 9293 78334 9982 9961 9982 9730 8880 9435 78635 9999 9981 9996 9793 8933 9580 79046 100 9989 9999 9893 8921 9687 7928



1 9956 9848 9945 9133 7613 8596 64042 9966 9949 9960 9550 8579 9142 70383 9972 9952 9967 9682 8796 9271 72634 9989 9965 9985 9726 8892 9329 74135 9999 9981 9999 9786 8913 9467 75046 100 9995 9999 9886 8892 9500 7558



1 9958 9842 9945 9133 6413 8596 74042 9970 9948 9958 9350 7679 9242 70383 9987 9962 9980 9482 8396 9311 72634 9997 9966 9988 9586 8792 9329 74135 9999 9991 9992 9586 8813 9407 75046 100 9996 9997 9786 8892 9450 7558



1 139 148 162 311 1089 601 11552 068 087 087 161 793 311 6993 054 062 058 100 479 200 5194 023 041 038 198 400 198 400



1 032 068 062 143 1093 448 5582 008 047 047 118 643 274 3313 004 032 028 113 413 180 2244 003 021 018 081 370 138 1945 001 018 010 078 315 111 1686 0006 010 008 067 256 095 158



















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6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




085

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1 9431 9412 9415 9100 7500 8467 65332 9822 9633 9744 9633 8333 9300 73003 9836 9846 9822 9767 8700 9533 77674 9982 9871 9882 9878 8750 9575 7788





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1 9537 9625 9545 9333 7533 8783 68002 9832 9738 9752 9517 8300 9367 75673 9846 9840 9862 9783 8783 9617 80834 9981 9890 9882 9891 8896 9783 8345



1 9737 9725 9645 9433 8033 8483 76062 9845 9738 9752 9717 8100 9369 78603 9866 9868 9872 9883 8287 9517 79634 9988 9888 9879 9863 8300 9530 8000




1 9941 9748 9838 9100 7767 8580 75842 9962 9943 9953 9500 8513 9142 78083 9970 9958 9972 9600 8740 9293 78334 9982 9961 9982 9730 8880 9435 78635 9999 9981 9996 9793 8933 9580 79046 100 9989 9999 9893 8921 9687 7928



1 9956 9848 9945 9133 7613 8596 64042 9966 9949 9960 9550 8579 9142 70383 9972 9952 9967 9682 8796 9271 72634 9989 9965 9985 9726 8892 9329 74135 9999 9981 9999 9786 8913 9467 75046 100 9995 9999 9886 8892 9500 7558



1 9958 9842 9945 9133 6413 8596 74042 9970 9948 9958 9350 7679 9242 70383 9987 9962 9980 9482 8396 9311 72634 9997 9966 9988 9586 8792 9329 74135 9999 9991 9992 9586 8813 9407 75046 100 9996 9997 9786 8892 9450 7558



1 139 148 162 311 1089 601 11552 068 087 087 161 793 311 6993 054 062 058 100 479 200 5194 023 041 038 198 400 198 400



1 032 068 062 143 1093 448 5582 008 047 047 118 643 274 3313 004 032 028 113 413 180 2244 003 021 018 081 370 138 1945 001 018 010 078 315 111 1686 0006 010 008 067 256 095 158



















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6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




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1 9537 9625 9545 9333 7533 8783 68002 9832 9738 9752 9517 8300 9367 75673 9846 9840 9862 9783 8783 9617 80834 9981 9890 9882 9891 8896 9783 8345



1 9737 9725 9645 9433 8033 8483 76062 9845 9738 9752 9717 8100 9369 78603 9866 9868 9872 9883 8287 9517 79634 9988 9888 9879 9863 8300 9530 8000




1 9941 9748 9838 9100 7767 8580 75842 9962 9943 9953 9500 8513 9142 78083 9970 9958 9972 9600 8740 9293 78334 9982 9961 9982 9730 8880 9435 78635 9999 9981 9996 9793 8933 9580 79046 100 9989 9999 9893 8921 9687 7928



1 9956 9848 9945 9133 7613 8596 64042 9966 9949 9960 9550 8579 9142 70383 9972 9952 9967 9682 8796 9271 72634 9989 9965 9985 9726 8892 9329 74135 9999 9981 9999 9786 8913 9467 75046 100 9995 9999 9886 8892 9500 7558



1 9958 9842 9945 9133 6413 8596 74042 9970 9948 9958 9350 7679 9242 70383 9987 9962 9980 9482 8396 9311 72634 9997 9966 9988 9586 8792 9329 74135 9999 9991 9992 9586 8813 9407 75046 100 9996 9997 9786 8892 9450 7558



1 139 148 162 311 1089 601 11552 068 087 087 161 793 311 6993 054 062 058 100 479 200 5194 023 041 038 198 400 198 400



1 032 068 062 143 1093 448 5582 008 047 047 118 643 274 3313 004 032 028 113 413 180 2244 003 021 018 081 370 138 1945 001 018 010 078 315 111 1686 0006 010 008 067 256 095 158



















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6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






1 9941 9748 9838 9100 7767 8580 75842 9962 9943 9953 9500 8513 9142 78083 9970 9958 9972 9600 8740 9293 78334 9982 9961 9982 9730 8880 9435 78635 9999 9981 9996 9793 8933 9580 79046 100 9989 9999 9893 8921 9687 7928



1 9956 9848 9945 9133 7613 8596 64042 9966 9949 9960 9550 8579 9142 70383 9972 9952 9967 9682 8796 9271 72634 9989 9965 9985 9726 8892 9329 74135 9999 9981 9999 9786 8913 9467 75046 100 9995 9999 9886 8892 9500 7558



1 9958 9842 9945 9133 6413 8596 74042 9970 9948 9958 9350 7679 9242 70383 9987 9962 9980 9482 8396 9311 72634 9997 9966 9988 9586 8792 9329 74135 9999 9991 9992 9586 8813 9407 75046 100 9996 9997 9786 8892 9450 7558



1 139 148 162 311 1089 601 11552 068 087 087 161 793 311 6993 054 062 058 100 479 200 5194 023 041 038 198 400 198 400



1 032 068 062 143 1093 448 5582 008 047 047 118 643 274 3313 004 032 028 113 413 180 2244 003 021 018 081 370 138 1945 001 018 010 078 315 111 1686 0006 010 008 067 256 095 158



















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(b)






6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





















0

1

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6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





6 Conclusion




References




































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



















Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



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