1DESIGN OF A HAND GEOMETRY BASED BIOMETRICSYSTEMA seminar reportSubmitted in the partial fulfillment of the requirements for the award ofDegree ofMaster of EngineeringInElectronics Instrumentation & Control EngineeringSubmitted By:******Roll No.********Under the supervision of:**********DEPARTMENT OF ELECTRICAL AND INSTRUMENTATIONENGINEERING*************
2ABSTRACTBiometrics which can be used for identification of individuals based on theirphysical or behavioral characteristics has gained importance in today’s societywhere information security is essential. Hand geometry based biometric systemsare gaining acceptance in low to medium security applications. Hand geometrybased identification systems utilize the geometric features of the hand like lengthand width of the fingers of the hand. The proposed system is a verification systemwhich utilizes these hand geometry features for user authentication. The systemaccepts a grayscale handprint from which it extracts the finger lengths and fingerwidths.
3ACKNOWLEDGEMENTThe real spirit of achieving a goal is through the way of excellence and austerousdiscipline. I would have never succeeded in completing my task without thecooperation, encouragement and help provided to me by various personalities.First of all, I render my gratitude to the ALMIGHTY who bestowed self-confidence, ability and strength in me to complete this work. Without his grace thiswould never come to be today’s reality.With deep sense of gratitude I express my sincere thanks to my esteemed andworthy supervisor Dr. Sunil Kumar Gupta for his invaluable guidance. It wouldhave never been possible to complete this work without his continuous support andencouragement. Inspite of his very busy schedule he was always approachable andavailable to attend to my problems, discuss the solutions, and give the appropriateadvice. Doing research under his supervision was a very enlightening andenjoyable experience. I also wish to thank all the faculty members of theDepartment of Electrical and Instrumentation Engineering for the invaluableknowledge they have imparted on me and for teaching the principles in mostexciting and enjoyable way. I also extend my thanks to the technical staff of thedepartment for maintaining an excellent working facility. I would like to thank myparents for supporting and taking me to this stage in life; it was their blessingswhich always gave me courage to face all challenges and made my path easier.**********Roll No.********
4TABLE OF CONTENTSCH. NO. TITLE PAGE NO.Abstract 2Acknowledgement 3Table of contents 4List of figures 51. Introduction 6-102. Literature review 11-163. Comparison of various biometric technologies 17-194. Proposed Work 20-25- MethodologyImage AcquisitionImage PreprocessingHand Feature ExtractionMatchingDecision5. References 26-28
5LIST OF FIGURESS.No. Figure Page no.1. Typical architecture of hand geometry biometrics 82. Diagram of a general biometric system 103. Equal Error Rate 154. Input image 225. Image after Binarization 226. Binarized image 247. Feature extraction 248. Template matching against templates in a database 25
71. IntroductionBiometrics, by definition, is a word whose origins related with the ancient greeklanguage. "Bios" which means life and "metron" which means measure. For thatreason biometrics is the emerging technology for distinguishing individuals basedupon recognition of one or more traits. Physical and behavioral characteristics areacquired by Biometric Systems. Biometric technologies are becoming thefoundation of an extensive array of highly secure identification and personalverification solutions.Typical architecture of all biometric systems consists of two phases:• Enrollment,• Recognition.In the phase of enrollment, several images of hand are taken from the users. Theimages, called templates, are preprocessed to enter feature extraction, where a setof measurement is performed.Final model depends on the method used for recognition. Models for each of theusers is then stored in the database. In the phase of recognition, a single picture istaken, preprocessed, and features are obtained. In the proposed system, the processof verification is used, where the input template is compared only with the modelof claimed person. The feature vector is compared with features from the modelpreviously stored in the database. The result is the person is either authorized ornot authorized.
8Figure1: Typical architecture of hand geometry biometrics
9Traditional hand geometry-based systems use low-resolution cameras or scannersto capture users’ hand images with the help of peggies or by forcing them to toucha screen. Those systems measure a hand shape to extract its features, like lengthsand widths of fingers, and hand contour, for recognition. Unfortunately, traditionaltechniques face unsolved problems: low discriminability due to low-resolutionhand images and bad user acceptability because users worry about hygienic issueswhen they have to touch screens.Because of the increased hygiene concern in biometric systems and the difficultyin recognizing fingerprints of manual laborers and elderly people, hand geometryhas been currently employed in many systems for personal verification mostly as acomplement to finger-print authentication.To improve discriminability and user acceptability, our new hand recognitionsystems have to acquire high-resolution hand images without peggy constraintsand also contacts. However, those new hand images rise new challenges, like handtexture, motion and shadow, to extract hand shapes, and measure hand features.Our project aims at those new challenges and gives a machine learning solution.A biometric system could have either or both of the two features, Identification andVerification. In the process of identification the individual presents the requiredbiometric characteristic and the biometric system associates an identity to thatIndividual. In the case of recognition or verification, however, the person presentsrequires both the biometric characteristic and an identity. The system then verifieswhether that identity is associated with that person’s biometric characteristic ornot. The proposed work aims to perform verification.
10Figure 2. Diagram of a general biometric system.
122.1LITERATURE REVIEWHand geometry is a biometric that identifies users by the shape of their hands.Hand geometry readers measure a users hand along many dimensions and comparethose measurements to measurements stored in a file.Viable hand geometry devices have been manufactured since the early 1980s,making hand geometry the first biometric to find widespread computerized use. Itremains popular; common applications include access control and time-and-attendance operations.Since hand geometry is not thought to be as unique as fingerprints or irises,fingerprinting and iris recognition remain the preferred technology for high-security applications. Hand geometry is very reliable when combined with otherforms of identification, such as identification cards or personal identificationnumbers. In large populations, hand geometry is not suitable for so-called one-to-many applications, in which a user is identified from his biometric without anyother identification.Nowadays it is easy to find biometric devices providing physical access to placesor logical access to computer data in several places from large companies to smallgyms. Hand geometry is a kind of biometric measure that is not as diffused in themarket as others, nevertheless in 2004 it took 11% of the entire market share forbiometric technologies (according to the International Biometric Group).Differently from most of the other systems , this work provides a new approachto the way hand geometry features are extracted. Data is read and processedindependently of the position of the user hand. This is done by analyzing thecurvature profile of the hand contour, making the feature extraction processrotation and translation invariant. Hand Geometry based human verificationtechnique which is efficient, simple, fast, easy to handle and cost effectivecompared to other verification techniques.Hand Geometry is a biometric key with medium level of individualization.Experiments show that the physical dimensions of a human hand containinformation that is capable to verify the identity of an individual. There are severalfeatures that can be extracted and used as key such as finger width and length,
13overall size of the hand, hand contour among others (a deep study about handgeometry features can be seen in ).Hand-based authentication schemes in the literature are mostly based ongeometrical features.For example, Sanchez-Reillo et al.  measure finger widths at different latitudes,finger and palm heights, finger deviations and the angles of the inter-finger valleyswith the horizontal. The twenty-five selected features are modeled with Gaussianmixture models specific to each individual.Öden, Erçil and Büke  have used fourth degree implicit polynomialrepresentation of the extracted finger shapes in addition to such geometric featuresas finger widths at various positions and the palm size. The resulting sixteenfeatures are compared using the Mahalanobis distance.Jain, Ross and Pankanti  have used a peg-based imaging scheme and obtainedsixteen features, which include length and width of the fingers, aspect ratio of thepalm to fingers, and thickness of the hand. The prototype system they developedwas tested in a verification experiment for web access over for a group of 10people.Bulatov et al.  extract geometric features similar to [3,4,5] and compare twoclassifiers.The method of Jain and Duta  is somewhat similar to that they compare thecontour shape difference via the mean square error, and it involves fingersalignment.Lay  introduced a technique where the hand is illuminated with a parallelgrating that serves both to segment the background and enables the user to registerhis hand with one the stored contours. The geometric features of the hand shape arecaptured by the quadtree code.Finally let’s note that there exist a number of patents on hand information-basedpersonnel identification, based on either geometrical features or on hand profile.
142.2. Advantages• Acquisition convenience and good verification performance• Suitable for medium and low security applications.• Ease of integration.• Currently being used for functions such as access control, employee timerecording and point of sale applications.• Reasonably high acceptance among users and it is opt-in.• Works in challenging environments.• Low template size, which reduces storage needs.2.3. Disadvantages• Large size of hand geometry device.• Limiting the applications of hand geometry system to verification task only.• Single hand use only.• It is not highly unique.• Weather, temperature and medical conditions such as pregnancy or certainmedication can affect hand size.• Hand size and geometry changes over time, especially in the very young and veryold.
152.4. United States Government EvaluationsThe US government has sponsored two evaluation of hand geometry technology.The 1996 Evaluation of the INSPASS Hand Geometry Data determined the effectof a threshold on system operation, established false accept and false reject rates asa function of the threshold, and presented and estimate of the Receiver OperatingCharacteristics (ROC) curve for the INSPASS system . The evaluators notedthat an estimate was the best that could be achieved with the available data. A 1991Performance Evaluation of Biometric Identification Devices evaluated the relativeperformance of multiple biometric devices, including hand geometry .The performance of a biometric system is measured in certain standard terms.These are false acceptance rate (FAR), false rejection rate (FRR) and equal errorrate (EER) also called crossover error rate (CER).FAR is the ratio of the number of unauthorized (unregistered) users accepted bythe biometric system to the total of identification attempts made.FRR is the ratio ofthe number of number of authorized users rejected by the biometric system to thetotal number of attempts made. Equal error rate is a point where FRR and FAR aresame.Figure3.Equal Error Rate
16False acceptance poses a much more serious problem than false rejection. It istherefore desired that the biometric system keep the FAR to the minimal possiblelimit. This can be achieved by setting a high threshold so that only very nearmatches are recognized and all else are rejected. The higher the securityrequirement from the system the higher the threshold required to maintain it.However FRR also depends upon the threshold. As the threshold increases theFRR increases proportionally with it. This is because due to a high thresholdmatches which are correct but below the threshold due to noise or other factors willnot be recognized. It is therefore desired that a balance is maintained. Usually thisbalance point is the ERR where the FRR and the FAR are equal. However thesecurity requirements from the system are the primary concern while deciding thethreshold value and either of the FAR or FRR might be sacrificed for the other. Incase of a very high security system the threshold may be raised while for a systemwhere false rejects are of more concern the threshold might be lowered.
183.1 Comparison of Various Biometric TechnologiesThe choice of a particular human characteristic to be used as a biometric traitdepends on the following criteria:Uniqueness is how well the biometric separates individually from another.Permanence measures how well a biometric resists aging.Collectability ease of acquisition for measurement.Performance accuracy, speed, and robustness of technology used.Acceptability degree of approval of a technology.Circumvention ease of use of a substitute.The following table shows a comparison of existing biometric systems interms of those parameters . A low ranking indicates poor performance in theevaluation criterion whereas a high ranking indicates a very good performance.Table1. Comparison between Different BiometricsTechnologycharacteristicFingerprint Iris Facial HandHow it works CaptureAnd comparesFingertippatternsCaptureAnd comparesiris patternsCaptureAnd comparesfacial patternsMeasuresAnd comparesdimensions ofhand andfingersCost of device Low High Moderate ModerateEnrollmentTimeAbout 3minutes,30 secondsAbout 2minutes, 15secondsAbout 3minutesAbout 1minuteTransactiontime9-19 seconds 12 seconds 10 seconds 6-10 seconds
19False nonmatch rate.2%-36% 1.9%-6% 3.3%-70% 0%-5%False matchrate0%-8% <1% .3%-5% 0%-2.1%Variabilitywith agesStable Stable Affected byagingStableCommercialAvailabilitysince1970s 1997s 1990s 1970sAs can be seen in this Table, each and every individual technology has limitationeither in universality, uniqueness, permanence, collectability, or performance,acceptability, circumvention. Due to these limitations, no single biometric canprovide a desired performance and the usage of multimodal biometric traits soundspromising.. Exploiting information from multiple biometric sources or featuresimproves the performance and also robustness of person authentication .One ofmost widely reported multimodal biometric authentication is combination ofspeech and signature features. Research shows that they result in goodperformance, but limited applications. Perhaps they didn’t collect the data frompractical environment. So, that’s still far from public applicability.
214. METHODOLOGYHand geometry recognition is based on the extraction of a hand pattern thatincorporates parameters such as finger length, width, thickness, curvatures, orrelative location. Hand geometry refers to the geometric structure of a hand, whichincludes lengths of fingers, widths at various points on the finger, diameter of thepalm, thickness of the palm, etc. . These features are not as discriminating asother biometric characteristics (such as fingerprints), however they can easily beused for verification purpose.The algorithm to extract the feature involves the following steps:Image AcquisitionImage PreprocessingHand Feature ExtractionMatchingDecision4.1 Image AcquisitionImage acquisition is the first step in a hand geometry biometrics system. Theimage acquisition involves capturing and storing digital images from visionsensors like color digital cameras, monochrome and color CCD cameras, videocameras, scanners, etc.The image acquisition system comprises of a light source ,adigital camera/scanner. The input image is a color/grayscale image of a hand. Inthe proposed system images are acquired through a digital camera. It is necessarythat the fingers are separated from each other. However it is not required to stretchthe fingers to far apart as possible. The hand should be placed in a relaxed statewith fingers separated from each other. Since features such as length and widthwhich are dependent on the image size and resolution are being used, it is criticalthat to have uniform size of images.There are various formats stored for the images such as .jpeg,.tiff,.png,.gif and.bmp. The captured images are stored in one of the following formats on thecomputer for possible image processing. The input image, shown in Figure3 isstored in .png format.
224.2 Image PreprocessingThe next stage is image preprocessing module. Image preprocessing relates to thepreparation of an image for later analysis and use. Images captured by a camera ora similar technique are not necessarily in a form that can be used by image analysisroutines. Some may need improvement to reduce noise; other may need to besimplified, enhanced, altered, segmented, filtered, etc.The role of the processingmodule is to prepare the image for feature extraction.Image preprocessing module consists of following operations:(i) Gray scale image(ii) Noise removal(iii) Edge detection(i) Grayscale imageThe first step in the preprocessing block is to transform the color image into agrayscale image. A red, green and blue (RGB) value of each pixel is extracted.Since a monochromatic image is required for the proposed system a threshold isdetermined. All pixels with RGB values above the threshold are considered whitepixels and all pixels below the threshold are considered black pixels. Initially thethreshold is set very low, very close to the RGB value of a black pixel in theimage. This produces an image with a completely white palm on a blackbackground as shown in Figure 4. Features such as finger lengths perimeter andarea of the palm can be more easily extracted from this image. However setting thethreshold very low results in a lot of noise in the image. A good threshold isdetermined and then noise removal algorithms are applied to the image.Figure 4: input image Figure 5: Image after Binarization
23(ii) Noise RemovalIdeally the scanned input image should contain no noise. However due to dust anddirt both on the palm and on the scanner bed, even in minute quantities mayproduce differences between the actual image scanned and the palm print. Thesevariations may also be the result of a host of other factors including the settings ofthe scanner, the lighting effects, humidity in the atmosphere etc. These variationsunless removed adversely affect the performance of the system. The larger thedegree of variations or noise the less accurate the system. So before extractingfeatures from the image, noise is reduced as much as possible. However most noiseremoval algorithms also affect the actual features so a balanced approach is takensuch that the features are undamaged after noise removal. Background lightningeffects and the noise make fake pixels in the image. MATLAB function imfilter isused to remove these pixels and to justify edges of the hand in the next step. Thefunction provides filtering of multidimensional images. The imfilter functioncomputes the value of each output pixel using double-precision, floating-pointarithmetic. Input image pixel values outside the bounds of the image are assumedto equal to the nearest array border value. Hand boundary is easily locatedafterwards.(iii) Edge DetectionIn order to extract geometric features of the hand it is required that the imagecontains only edges. Edge detection is the process of localizing pixel intensitytransitions. The edge detection has been used by object recognition, target tracking,segmentation, etc.An edge is a collection of connected high frequency points in animage.Visually,an edge is a region in an image where there is a sharp change inintensity of an image. Detecting edges of an image represents significantlyreduction in the amount of data and filters out useless information, whilepreserving the important structural properties in an image.Therefore, the edge detection is one of the most important parts of imagepreprocessing. There mainly exist several edge detection methods like Sobel,Prewitt, Robets, and Canny.
244.3 Hand Feature ExtractionThere are several features that can be extracted from the geometry of the hand.Each finger has three major lines running perpendicular to the length of the finger.The first feature that can be extracted is the length of a finger which is defined asthe distance between the tip of the finger and bottommost line on the finger. Thesecond major feature is the width of the finger. One or more measurements can betaken for the width at varying points along the finger. The length of the lines on thefinger can also be used as the measure of finger width. Since the fingers may nothave uniform width usually two or more measurements are taken for each fingeralong different points.Figure 6: Binarized image Figure7: Feature Extraction4.4 MatchingThe matching stage provides the means to determine the identity of a user. When auser attempts recognition in a biometric system, the user’s generated featurestemplate will be compared against the templates stored in the database.In one-to-one verification, this comparison is done only against the claimedidentity’s template, whereas in a one-to-many identification it is done against theentire database. Since the case of verification is just a subset of the identification
25case, only the later is described and reported in this work – verification willtypically yield better results.The matching stage is based on a classification algorithm that generates a distancescore for each template comparison using a feature vectors’ similarity measure.The score with the lowest distance value indicates the best match. Unnecessarytemplate matching comparisons are avoided by also taking into account if thetemplates being compared both belong to the right or left hand, information whichis obtained from the pre-processing stage.4.5 DecisionAfter running the matching algorithm, a recognition decision is made whether toaccept or reject the best match found. If the distance score exceeds a predefinedthreshold, the recognition attempt is considered as an impostor access, otherwisethe recognition attempt is considered a client access and the system assumes theuser has been correctly identified. The classification procedure is illustrated inFigure 9.Figure 7: Template matching against templates in a database.
27 R. Sanchez-Reillo, C. Sanchez-Avila, A. Gonzalez-Marcos,“BiometricIdentification through Hand Geometry Measurements”, IEEE Trans. On PatternAnalysis and Machine Intelligence v22 n10, pp. 1168-1171, 2000.. S; Travieso C.M.; Alonso, J.B.; Ferrer M.A.; "Automatic biometricidentification system by hand geometry", IEEE 37th Annual 2003 InternationalCarnahan Conference on 14-16, 281 – 284, Oct. 2003. R. Sanches-Reillo, C. Sanchez-Avila, and A. Gonzalez-Marcos, “BiometricIdentification through Hand Geometry Measurements,” IEEE Transactions ofPattern Analysis and Machine Intelligence, Vol. 22, No. 10, October 2000. C. Öden, A. Erçil and B. Büke, "Combining implicit polynomials andgeometric features for hand recognition", Pattern Recognition Letters, 24,2145-2152, 2003. . A.K. Jain, A. Ross and S. Pankanti, "A prototype hand geometry basedverification system", Proc. of 2nd Int. Conference on Audio- and Video-Based Biometric Person Authentication, pp.: 166-171, March 1999. Y. Bulatov, S. Jambawalikar, P. Kumar and S. Sethia, "Hand recognitionusing geometric classifiers", DIMACS Workshop on ComputationalGeometry, Rutgers University, Piscataway, NJ, November 14-15, 2002. A.K. Jain and N. Duta, "Deformable matching of hand shapes forverification, Proc. of Int. Conf. on Image Processing, October 1999. Y. L. Lay, “Hand shape recognition,” Optics and Laser Technology, 32(1),1–5, Feb. 2000. R.L. Zunkel, “Hand Geometry Based Verification”, pp. 87-101, inBiometrics, Eds. A. Jain, R. Bolle, S. Pankanti, Kluwer AcademicPublishers, 1999. James Wayman, ed., “National Biometric Test Center Collected Works,”San Jose State University, August 2000.http://www.engr.sjsu.edu/biometrics/nbtccw.pdf.
28 James Holmes, Larry Wright, and Russell Maxwell,” A PerformanceEvaluation of Biometric Identification Devices,” Sandia National Laboratories,June1991.http://infoserve.sandia.gov/cgi-bin/techlib/access-control.pl/1991/910276.pdf. Comparisons of Various Biometric Technologies,www.biometricvision.com M. N. Eshwarappa and M. V. Latte, Bimodal Biometric PersonAuthentication System Using Speech and Signature Features, International Journalof Biometrics and Bioinformatics, (IJBB), Volume (4): Issue (4) R. S. Chora´s, M. Chora´s, ”Multimodal Hand-Palm Biometrics,” inAdaptive and Natural Computing Algorithms, Springer Verlag LNCS 4432, pp.407 - 414.