International Research Journal of Engineering and Technology (IRJET)
Volume: 09 Issue: 04 | Apr 2022 www.irjet.net
Signature Verification System using CNN & SNN
e ISSN: 2395 0056
p ISSN: 2395 0072
Robin Nadar1 , Heet Patel2 , Abhishek Parab3, Akhilesh Nerurkar4, Ruchi Chauhan5
1,2,3,4 B.E. Student, Electronics and Telecommunication, Atharva College of Engineering, Mumbai, India
5 Professor, Electronics and Telecommunication, Atharva College of Engineering, Mumbai, India
***
Abstract - Signature verification is the most rudimentary method for identity validation. Signatures can be verified in many ways however the MachineLearningalgorithmsprovide the best method for verifying a signature whilst comparing it to the original signature of the person in question. Criminal acts of forging a signature on financial documents, checks, exampapers, consent forms, etc., arenot uncommonandarea detriment to an organization. Signatures carry many traits and quirks unique to the individual creator. Character/alphabet spacing, dots, curves, and many other such parameters can be determined with Neural networks, allowing for a threshold to be created for comparison of original signatures to the ones in objection. The proposed work is a Signature Verification algorithm hosted as a web application that uses Convolution Neural network and Siamese Neural Networks in conjunction. Our objective is to produce a systemwithhighaccuracyandforuserconvenience make the UI with the best possible accessibility.
Key Words: Signature verification system, CNN, SNN, CNN & SNN, Convolutional, Siamese, Neural Networks, Deep Learning.
1. INTRODUCTION
Identityimpliestheuniquenessorindividualityofaperson, somethingthatsetshimapartfromhispeerssothathecan beuniquelyidentifiedforwhoheis.Itsimportanceishuge becauseotherwise,societymightfallintochaos.Anameisa commonidentifier.Identityverificationissimplythecross checking of the identity of a person to ensure that he is indeedthesamepersonthathisidentificationtellshimtobe. It is necessary in various sectors like banking, insurance, medical,government,etc.toensureorderintheworkingof theorganizationandpreventfraudandothersuchcrimes.
Aperson’suniqueidentitycanbeprovedwiththehelpofhis physiologicalcharacteristicswhichinlegal/technicalterms canbereferredtoasBiometrics.Themostcommonlyused biometric features for identification are fingerprint, iris, voice,andhandwrittensignatures.
Fingerprint identification is the physical process of authenticationusingthefingerprintscannerconvertingthe fingerprintintodigitalcodeandoptimizingit.Wedon’thave to remember complex passwords but sometimes due to injuries,itcaninterferewiththescan.Usinginkprintsisalso apossiblemethodbutkeepingitsrecordsrequiresphysical documents which makes it a bulky, easily perishable, and inconvenientmethod.
2022,
InIrisrecognition,theiris,locatedbetweentheeyelashesis scannedusinga high poweredcamera andconvertedinto digitaldata.Astheirisisaninternalorganandhassensitive membranes it cannot be injured easily, making it highly unlikely to influence verification. But this technology requiresspecializedhardwareandsoftwaremakingitcostly, unwieldy, fragile, and unable to be implemented everywhere.
Voice recognition works by recording the voice and analyzing its various features like pitch, tone, accent, speakinghabits,etc.,andmatchingittoanothervoicetobe verified.Voiceisdifficulttofalsifyforacommonperson.But this process requires clear voice samples for proper verification and it forbids any external noises that can interferewiththeverificationprocess.Also,theequipment thoughrobustisbulkyandthestoragerequirementislarge.
Themainadvantagesofhandwrittensignaturesoverother meansofidentityverificationare:
•It can be performed anywhere with a simple pen and paper
•Fast,easy,andcheapcomparedtoothermeans
•No specialized gadgets or instruments are necessary, though could be used for better results and user experience
•Beingliterateisnotnecessaryforthismeansofidentity verificationifthesignatureismemorized
•Itshistoryofseveralcenturieshasspreadthismethodto allcornersoftheglobesoitisauniversallyknownand trusted method that everyone is aware of and used to, unlikethemorerecentmethodsormethodsthatareused only in certain regions or amongst certain groups or circles
An identity verification system works on the principle of individual identification based on the unique traits of the said individual as discussed previously. The advantage of usingsuchasystemovermanualverificationisthattheyare cheaper in the longer term, accuracy is higher, can be installedanywhereandavailabilityisensured.
A signature verification system is a computerized or mechanizedsystemthatcomparesanoriginalsignaturewith asignaturethatneedstobeverified.Usingimageprocessing algorithms, it compares the various features that are pre programmedintothesystemandgivesanoutputbasedon
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predeterminedparameterswhetherthesignatureisgenuine oraforgery.
Comparedtotheotherverificationsystemsitrequireslow storageandhasafastresponse.Butincaseofaninjuryor inabilitytomakeasignatureproperly,orincaseofpeople havinginconsistentsignatures,usingthistypeoftechnology foridentityverificationisnotpossibleandwehavetoresort toothermethods.Also,thismethodonlyrequiresasingle computer system in case the scanned images of the signaturesalreadyexist,butwillrequireacamera,scanner, orstylusinputotherwise.
The ease of access is also a concern as, if the system is installedonadevicethatiscurrentlyinaccessibletoauser due to any reason it will cause undesirable user inconvenience.Internetsolvesthisproblem.Asystemthat worksonline,canbeaccessedthroughanydeviceconnected to the internet solving the problem of accessibility and storage.
ASiameseNeuralNetworkisatypeofneuralnetworkthatis used to compute the similarities and differences between twoseparatesetsofinputdata.Itismadeupoftwoidentical sub networks(whichmaybeanysortofneuralnetworks, suchasConvolutionalNeuralNetworks(CNN)[15],Single LayerorMulti LayerPerceptrons(MLP),RecurrentNeural Networks (RNN), and so on) each having a distinct input node and a shared output node. The two identical subnetworkscollaborateontwoseparatepiecesofdatato producetwodistinct sets of outcomes.These twooutputs arecomparedtogetaresultdefiningthedegreeofsimilarity betweenthetwostartinginputs.
4. ARCHITECTURE [1]
Fig 1. BlockDiagram
2. PREPROCESSING [1]
Weallknowthatimagescomeinavarietyofpixelsizesand values.Grayscaleimagesarecreatedbyconvertingordinary color images to grayscale ones. Because batch training a neuralnetworknormallyrequiresimagesofthesamesize, the signature images we evaluate range in size from 153x258to819x1137.Itisvitaltoestablishauniformsize for input images in order to get effective results in image processing.Asaresult,weusebilinearinterpolationtoscale alloftheimagestoaconstantsizeof155x220.Theimages are then inverted such that the background pixels have 0 values.Inaddition, wenormalizeeachpicture bydividing thepixelvaluesbythestandarddeviationofthepixelvalues oftheimagesinthecollection.
3. CONVOLUTION NEURAL NETWORK & SIAMESE NEURAL NETWORK (CNN & SNN) [1][2][3][4][5][6][7]
AConvolutionalNeuralNetworkisaneuralnetworkthatis constructed by combining any number of data processing layers centered on a convolutional layer. A CNN used for Image Processing isoftendivided intotwostages:feature extractionandfeaturecategorization.Theconvolutionlayer ismadeupofanetworkofnumerouslearnableconvolution kernels(alsoknownasfilters)thatcomputefeaturemaps. When an elementwise non linear activation function is applied to the convolution of the input with the kernel, a featuremapisgenerated.ACNNisoftenusedfortaskssuch asimageclassification,objectdetection,imagesegmentation, andsoforth.
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Fig 2. Architecture [3]
Thesizeofthefiltersforconvolutionandpoolinglayersis listedasNxHxW,whereNisthenumberoffilters,Histhe height,andWisthewidthoftheassociatedfilter.Strideis the distance between the application of filters for convolution and pooling processes, and pad denotes the widthofadditionalborderstotheinput.Paddingisrequired inordertoconvolvethefilterfromtheveryfirstpixelinthe inputpicture.RectifiedLinearUnits(ReLU)areusedasthe activation function for the output of all convolutional and fully connected layers throughout the network. Local ResponseNormalizationisusedwiththeparameterstohelp generalizethelearnedcharacteristics.WeemployaDropout with a rate of 0:3 and 0:5, respectively, with the final two poolinglayersandthefirstfullyconnectedlayer.
Theinitialconvolutionallayersuse96kernelsofsize11x11 withastrideof1pixeltofilterthe155x220inputsignature picture.Thefirstconvolutionallayer's(response normalized and pooled) output is sent into the second convolutional layer,whichfiltersitusing256kernelsofsize5x5.Thethird and fourth convolutional layers are linked to one another withouttheuseoflayerpoolingornormalization.Thethird layer has 384 kernels of size 3x3 that is coupled to the second convolutional layer's (normalized, pooled, and dropout) output. The fourth convolutional layer contains 256 kernels with a size of 3x3. As a result, the neural network learns fewer lower level information for smaller
receptivefieldsandmorehigher levelorabstractfeatures. The first completelylinkedlayerhas1024 neurons, while thesecondfullyconnectedlayercontains128neurons.This means that the largest learned feature vector from either sideofourModelhasasizeof128.
This framework has been utilized successfully in weakly supervisedmetriclearningfordimensionalityreduction.At the top, a loss function computes a similarity metric incorporating the Euclidean distance between the feature representations on either side of the Siamese network, whichconnectsthesesubnetworks.Thecontrastivelossisa commonlyusedlossfunctionintheSiamesenetwork,andit isdefinedasfollows:
L(s1,s2,y)=α(1−y)D2w+βymax(0,m−Dw)^2………(1)
wheres1ands2aretwosamples(heresignatureimages),
y is a binary indicator function denoting whether the two samplesbelongtothesameclassornot,
αandβaretwoconstantsandmisthemarginequalto1in ourcase;
Dw=f(s1;w1)−f(s2;w2)………(2) istheEuclideandistancecomputedintheembeddedfeature space,
fisanembeddingfunctionthatmapsasignatureimageto realvectorspacethroughCNN, andw1,w2arethelearned weightsforaparticularlayeroftheunderlyingnetwork.
This space will have the feature that pictures of the same class (authentic signature for a particular writer) will be closertoeachotherthanimagesofotherclassesduetothe loss function used (Eqn. 1). (forgeries or signatures of differentwriters).AlayercomputestheEuclideandistance betweentwolocationsintheembeddedspaceandconnects both branches. Then, to assess if two photos are similar (genuine, genuine) or dissimilar (genuine, fabricated), a thresholdvalueonthedistancemustbedetermined.
5. METHODOLOGY [8][9]
Thresholding in a Signature Verification System is the method of identifying an input signature as genuine or forged by comparing its dissimilarity ratio to a predeterminedthreshold.Thedecisionthresholdisusedto maintainabalancebetweenthemoststableandleaststable signatures.Ifthedissimilarityratioislessthanthethreshold, theinputisconsideredgenuine;otherwise,itisclassedasa forgery.
Thelengthofalinesegmentbetweentwospecifiedlocations represents the Euclidean distance between two points in Euclidean space, as we know from mathematics. The Euclidean Distance is the distance between two signature characteristics in a Signature Verification System. The
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characteristics might include Critical Points, Center of Gravity,Slope,andsoon.Assumingthatallofthefeatures present in the original signatures are also present in the querysignature,theEuclideandistanceiscalculated,andif thequerysignatureimage'sEuclideandistancewithrespect to the mean signature is within the set range, the query signatureisgenuine;otherwise,itisclassifiedasforged.
FARANDFRRThefollowingerrorratesareusedtoassesstheperformance ofbiometricsystems:
False Acceptance Rate (FAR): The percentage of identificationinstancesinwhichunauthorizedpersonsare incorrectlyaccepted.
FalseRejectionRate(FRR):Thepercentageofidentification instances in which authorized persons are incorrectly rejected.
As the number of false acceptances (FAR) decreases, the numberoffalserejections(FRR)increases,andviceversa (seethefigureabove).Theintersectionofthelinesisalso known as the Equal Error Rate (EER). This is the point at which the percentage of false acceptances and false rejectionsisthesame.
6. REQUIREMENTS
6.1 HARDWARE REQUIREMENTS
Laptop/PC
Processor:Inteli5processororabove
GraphicsCard:AMDorNVIDIAatleast2GB
RAM:Morethan4Gb
Storage:1Gbdiskspace
Internetconnection:Above10mbps
Fig 3. CharacteristicsofFAR&FRRVolume: 09 Issue: 04 | Apr 2022 www.irjet.net
SOFTWARE REQUIREMENTS
OperatingSystem:64BitOperatingWindows10or higher
Python(3.8.8)
VisualStudioCode
7. WORKING OF THE WEBSITE
Fig 4. WebsiteFlowchart
Thefigurebelowshowstheguestpageofoursystemwhere anindividualcanverifytheirsignature.
Thisshowsthatthesignatureisauthentic.
Fig 5. GuestUserAuthentic
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Thisshowstheimageisnon authentic
e ISSN: 2395 0056
ISSN: 2395 0072
Fig 6. GuestUserNotAuthentic
Thefigurebelowshowsthemainpageofoursystemwhere an individual can register their details and create a permanentprofile.
Thisshowsthatthesignatureismatching
Fig 7. AuthenticProfileUser
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Thisshowsthatthesignatureisnotmatching
8. NotAuthenticProfileUser
8. TABULATION
Table 1: AccuracyTable
Sr. No Datasets Signers Accuracy FAR FRR
1. Cedar 55 86.56% 13.44 13.44
2 Self made 10 85.36% 14.64 14.64
9. RESULT
Signature Verification System, like any other Human Verification System, is not fully infallible, and there is a probabilitythatitmayprovideincorrectresultsregardless ofthetechniqueormethodologyused.However,whenwe comparethevariousSignatureVerificationSystems,wecan findthatCNNandSNNarethemostoftenutilizedtechniques owingtotheireaseofdevelopment,usage,andaccuracy.The modelhasanaccuracyof86%witha3%tolerance.
Also, this system has a guest page where the user can validate a questionable signature immediately without creating a profile. On the other hand, the profile system allowstheusertopermanentlysavetheirvalidsignatureson the system which allows them for quick verification and savestheusertheinconvenienceofuploadingthesignatures again.
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