2003automatic biometric identification system by hand geometry

8/7/2019 2003AUTOMATIC BIOMETRIC IDENTIFICATION SYSTEM BY HAND GEOMETRY

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AUTOM ATIC BIOMETRIC IDENTIFICATION SYSTEM BY HAND GEOMETRYSantiago Gonzalez, Carlos M. Travieso, Jeslis B. Alonso, Miguel A . Ferrer

@to. de S d e sy Comunicaciones, Universidad de Las Palmas de G m CanariaCampus de T a k 35017 Las Palmas de Gran Canaria, SpainPhone: +34 928 452 864 Fax: +34 928 451 243 e-mail: [email protected]

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ABSTRACTIn this paper we propose a novel and simple method to

recognize individuals based on their hand-palm geometry.A compact set of parameters has been extracted andreduced using different transformations. Twoclassification methods have been implemented: NeuralNetworks (NN) based on the commonly used MultilayerPerceptron (MLP) and the m ost nearby neighbor classif ier(KNN). Results show that not complex algorithms arerequired in the classification phase to obtain highrecognition values. In ou r simulations, rates beyond 99%have been achieved.

1. INTRODUCTIONBiometric identification systems have experimented anexplosive growth in devices concerning with securityapplications; they lead a privileged position in the chosenmethods used to identify or verify the identity o f a userpresented to the system. Among all existing methods,mayhe the use of the geometry of the han d is actually oneof the most investigated and employed ones due to theirreliability, efficien cy and facility to use [1][2].The aim of our work is to develop a simple andeffective recognition system to identify individuals usingthe palm of their hands. We are going to utilize simple andacquaintance classifiers as Neural Networks (NN) [3], andthe most n earby neighbor classifier (KNN) [2].

The first step is to elaborate a database containing aset of characteristics from the users. This database hasbeen b uilt off-line, and contains 50 0 sample s taken from 50different users. Two main parameters are extracted fromthe user 's hand-palm: geometrical and co nto w information.

The database is then pre-processed with th e purposeto prepare the images fo r the parameter extraction phase.This process is composed by four main stages:binarization, contour and main points extraction (fingertips and valleys between fingers) , area and perimetercalculation, and finally a contour normalization of the hand[41.

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Contour information generated in the previous stepis too large to be efficiently used and classified with theproposed methods in our sys tem thereby i t has to bereduced. Several techniques for reduction has been usedand compared. The objective is to reduce the length o ft hefeatures keeping as much information as possible; amongthese techniques we can find the simplest obtained bysub-sampling the feature'space, image descriptors using awavelet decomposition [5] or more advanced techniquesas Principal Component Analysis (PCA) based ontransform dom ain techniques.

We w ill classify both sets of features (geometric andcontour based) using the two classifiers alreadycommented Neural Networks and the most nearbyneighbor classifier (KNN). Comparison of which of theclassification techniques are more suitable for every set ofparameters can he drawn from the results.

In this paper, we, f irst, present how the database waselaborated and the image processing needed to be able toextract fhrther parameters. Then details and procedu res fo rextracting the features are shown. The classificationprocess and results are presented in sections fourth andfifth. Finally in the last two sections conclusion andfurther references are given.

2. DATABASE ELABORATION AND IMAGEPROCESSING

In this section we describe the characteristics o f thedatabase, the steps performed in its realization andcomposition, and several pre-processing techniquesutilized. The database is composed by 10 scanned handsamples taken from 50 different individuals. Th is databasehas been built off-line, the user has to be enrolled in the

TableI : Imape specifications.

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system before heishe can use it for the very first time. Forthe image acquisition, it has been utilized a desktopscanner with a resolution of 150dpi, the origin al size of thehand has been reduced in a 20% to facilitate subsequentcalculations and processes on the image. The main imagespecifications used in building the imag e database can beobserved in the table 1.

As an example, Figure 1shows a sample image takenfrom the database.

Figure 1. Hand Image extracredfrom the database.Onc e the database is created, we should process i t totransform the images into a suitable format to facilitate

further param eter extraction. From the grey scale imageswe obtained a new image containing the contour or theoutline of the hand. Tw o fundamental steps were followedto achieve these objectives:Binarization of the image: During the search of the

optimum threshold, it has been proved differentmethods as the ones suggested by Lloyd, Ridler-Calvar and Outsu [ 6 ] .The tw o first algorithms gave usa threshold very similar (around value of intensity100); and the third a threshold of 185. The last value

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totally was ruled out, since the processed image, on ceapplied the threshold of 185 was worse than the caseof high threshold shown in the Figure 2. For the valueof 100 did not exist many differences, since smallvariations in the election of the threshold do not affectin an important way the final results. Therefore, thesearch of a threshold is a heuristic process resulting i na threshold place in the range of 65-100.Compute of the hand contour: Using the binarizedimage, we run through the hand silhouette byfollowing the image's edge. The monitoring d th econtour is a procedure which sweeps through thehand outline, distinguish between object (hand) andunderground. The implemented algorithm is amodification of the algorithm created by Sonka, Hlavacand Boyle [ 7 ] . In the original algorithm, the authorsuse a description of the contour based on 4 values(0,1,2,3) to codify the direction of each point located inthe contour of the hand. In our implementation, wehave taken into account 8 possible changes ofdirection. The result of this process can be observedin the followin g figure;

Figure 3. Contour extracted of rhe binarizared image.The last step in the pre-processing phase, it is to

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t to their centroid, giving7a01im 200 310 400 xm ECQ no ECQ w:sired translation invariance.

3. PARAMETFREXTRACTIONDifferent techniques have been undertaken to determinewhich ones provide higher identif ication rates. Twocompletely different sets of parameters are extracted fromthe images.

Geometric Measurements: 10 direct features areextracted from every image: Length of the fingers,3 hand ratio measurements, area and perimeter.

(a)High threshold (4, Selected threshold 282Figure 2. Process ofbinarizorion.


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Figure 4 shows the geometric measurementsgraphically.

Figure4. Contour an d measurements extractedfrom the imagetaken as example.Contour information: Besdes the direct

information obtained from the hand. We can useextra information from the contour. In order toreduce the set of parameters we proposed to usea module representation of the co ntour (distancefrom the origin to every point in the contour).This process provides us with a wave shapesignal (Figure 5 ) particular to every user.

1

Figure S.Gut/ine of the hand and representation of the module.Another form of parameterise has been from thecontour of the hand, sweeping by polar coordinates

(module an d phase) [7]. A typical problem found when weuse this technique is the length of template vector.Generally it is desirable to find or to reduce the set ofcharacteristics, to one new vector that be minimum hutsufficient. This it can he achieved by means of a judiciouselimination, because the elements that compose thisvector would be enough related between them. In order toreduce the length of template vector, the next techniquesare:Principal Component Analysis (PCA) [3]: PCA also

known as Ka hu nen Loeve transformation can he usedto reduce the feature vector dimension wh ile retainingthe feature information by constructing a lineartransformation matrix. The matrix of transformation iscomposed of the eigenvectors more significant of thecovariance matrix, formed from the vectors ofcharacteristics. The eigenvectors are orthonormals(orthogonal and normalized) therefore with them wewill transform the original data in independentcharacteristics having maximum variance.Wavelet transform: Those transform are mathematicalfunctions that divides the data in differentcomponents, and studies each component with anapprop riate resolution to their scale. The advantag e ofWavelet transformed on the traditional methods ofFourier is that it can analyse situations where thesignal contains interruptions and abrupt peaks [SI.This discrete Wavelet transformed (DWT) is a linealoperation that generates a structure of data thatcon tains log,(@ variable segme nts of length.Cosine transform: Inspired in the idea of thedescriptors of Fourier, we ask ou rselves if any ano thertransform could be used instead of the DFT to obtainbetter results. As it is known, the high informa tionredundancy present in the images turns out to beineffective when these images are used directly fortasks of recognition, identification and classification.To reduce this quantity of information we will useDiscrete Cosine Transformed (DCT) as a way ofcompaction of the energy in the signal. As it can beknown, Karhunen-Lotve Transformed (KLT) isoptimum transformed in terms of compaction o f energy[4], the problem is that the KLT is clerk of th e data andobtaining the base images of the KLT is in general avery high com putationa l task. The DCT has interestingproperties that can be taken as advantage [4][9], suchas that possess an excellent property of compaction ofenergy for highly correlated data and it is a fasttransform as the DFT.Recognition rates were computed for the following

features length: 128 and 512 values for sub-sampling, 50and 128 values for PCA, and 128 and 512 values forwavelets. With this parameter extraction, the success rates

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in user identification are shown in Table 1. Theseexperiments have been repeated 10 times, for averagingthe rates, being shown the results of the maximumrecognition rate, in function of the employed trainingpercentage (see the table 1).

4. CLASSIFICATIONTwo classifier methods have been utilised in ourrecognition system. In both cases, the learning wassupervised, creating a two-phase process in theclassification, the training a nd the test phase.The neuronal network utilized is a feedfonvard one;being training with the system of learning perceptronmultilayer (NNMLP)[3], by means of the algorithm backpropagation. A hidden layer containing 58 neurons hasbeen utilized giving us the best results.

Another utilized classif ier method is the m ost nearbyneighbour classifier (KNN)[Z]. This algorithm is one of th esimplest methods of classification. All in all, the portion ofcode of the algorithm stores the data that we present.When we want to do a predict ion on a new one vector ofcharacteristics, the K N N algorithm finds the vector morenearby (according to some metric distance) from .thetraining vectors to the new vector, and predicts the newclass. TheK is a method o f learning based on the mostnext neighbour. This algorithm calculates the similarityamong the sample of test and the samples of training,consider ing the h ec to rs more nearby in the tra ining,finding the class that m ore is seemed. To find the vector ofmore seemed training (not alone in distance), we use themethod of the majority of votes.The degree of similarity between two samples is thedistance among them, based on a metric distance. In ou rsimulations we have used the distance Eu clidean.

Be f a sample w ithn character is tic represented by thevector ofcharacteristics Cv,(t),v,(t),..,,w@$+ th eterm VI(@ the value of the characteristic one i of th esample f . Therefore the distance among two samples ti andt, a re d(ti,tt),where:

5 . EXPERIMENTSAND RE SUT SThe realized experiments hav e been carried out accordingto each classif ier , applying each one of the techniquescited previously (vector reduced and geometricparameters) . I t has been carried out varying thepercentage of training, with the intention of utilising anumber reduced of hands at the moment of to create thetraining, tried to discover which is the minimum. In th e

following table are show n the o btained results, expressingthe rate of reached success, in function of the type ofparameterization, classification method, length of theparameters and number of hand fo r training (like there is I Ohand, each hand is 10% oftraining).

Table I : Comparison behveen diferent techniques ofidenh$cation.

6 . CONCLUSIONSA biometric identif ication system has been proposed forpersons recognition based on the hand geometry, with avery high relation between simplicity and efficiency, sincewith 10 parameters we have ob tained a recognition rate ofth e 100% with our database, using the most nearbyneighbors classifier.

The rest of results are also appropriates, although itis obtained lower rate of success, with this, the rest of th emethod has good response, too, us in g both class if iers andvarying the percentage of training.

7. REFERENCES[ I ] Ani1 K. Jain, Ruud B o k , and Sharath Pankanti, Biometrics:Personal Identification in Networked Society Ed. Kluwer AcademicPublishers, 2001.[2 ] Ani1 K. Jain, Arun Ross, an d Sharath Pankanti, A PrototypeHand Geometly-based Verification System, Proc. of 2nd Int.Conf. on Audio- and Video-based Biometric Person Authentication,Washington D.C., pp.166-171, March 22-24, 1999.[3] C. M . Bishop, Neural Networks /or Pattern Recognition, Ed .Oxford University Press, 1995.[4] Ani1 K . Jain , Fundamentals of Digitol Image Processing.Prentice Hall. 1989.[5] S . Bow, Pattern Recognition and Image Preprocessing, Ed.Marcel Dekker, 1992.(61 Biilent Sankur, Mehmet Sezgin, Image thresholdingTechniques: A survey over categories.(visited in June 2002)h t t p : l l ~ . b u s i m . e e . b o u n . e d u . t r / - s a n k u r1.doC

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2003automatic biometric identification system by hand geometry

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