Signature VerificationHistoryCurrent State of the ArtSignature Verification vs. Handwriting RecognitionPattern Recognition SystemPattern Recognition SystemPattern Recognition ProcessPreprocessingResampleResamplingSmoothingStroke ConcatenationCritical PointsCritical PointsPreprocessing StepsPreprocessing StepsPreprocessing StepsPattern Recognition ProcessFunctional vs. ParametricFeature ExtractionLocal FeaturesLocal FeaturesTemporal FeaturesGlobal FeaturesPattern Recognition SystemString MatchingDTW ExampleString MatchingUser Dependent NormalizationDimension ReductionPCAValidation SetSVC ExamplesMahalanobis DistancePattern Recognition SystemScore FusionScore FusionWeighted Sum RuleBiometric ComparisonSignature VerificationHistory• Sumerians used intricate seals applied to clay cuneiform tablets to authenticate their writings.• Documents were authenticated in the Roman Empire (AD 439) by affixing handwritten signatures to the documents.• In 1677 England passed a an act to prevent frauds and perjuries by requiring documents to be signed by the participating parties.• In 1977, the first studies of both off-line and on-line signature verification algorithms were published– Nagel and Rosenfeld “off-line system” IEEE T. Comp.– Liu and Herbst “on-line system” IBM J. Res. Dev.• Much research has followed, attempting various methods for both feature extraction and matching– Yang et. al. “Application of Hidden Markov Models for Signature Verification” (HMM)– Lam et. al. “Signature Recognition through Spectral Analysis” (FFT)– Hangai et. al. “Writer Verification using Altitude and Direction of Pen Movement”(DTW)– Lejman et. al. “On-line Handwritten Signature Verification using Wavelets and Back-propagation Neural Networks” (Neural Network)– Crane et. al. “Automatic Signature Verification using a Three-axis Force-sensitive Pen”(Parametric)Current State of the Art• No common agreement on benchmark databases and protocols in the research community.• 1st International Signature Verification Competition in 2004 (100 users; 20 genuine and 20 forgeries):– Web: http://www.cs.ust.hk/svc2004/– On-line signature: 2 Tasks.– No companies participated (or participated but remained anonymous).– Difficult task (pen tablet without visual feedback, synthetic signatures, forgers were given the dynamics of the signatures to imitate, English and Chinese signatures, etc.).– Best system (3% EER Skilled Forgeries, 1.5% EER Random Impostors)• Human performance:– Expert (0.5%FAR @ 7%FRR), Layperson (6.5%FAR @ 26%FRR)Signature Verification vs. Handwriting RecognitionPaola GarciaHandwritingRecognitionSignature Recognition(Verification)• On-Line:• Off-LineAltitude (0°-90°)90°270°0°Azimuth (0°-359°)180°XYPAz0 100 200 300AlSCANNERApplicationsApplications• On-line:–SOFTPRO (http://www.signplus.com/)– CYBERSIGN (http://www.cybersign.com/)– CIC (http://www.cic.com/)•Off-line:– APP-DAVOS (http://www.app-davos.ch/)– NUMEDIA (http://www.sapura.com.my/NuMedia/check.htm)• Advantages of signature verification:– User-friendly.– Well accepted socially and legally.– Non invasive.– Already acquired in a number of applications.– Acquisition hardware:• Off-line: ubiquitous (pen and paper).• On-line: inexpensive and already integrated in some devices (TabletPC).– If compromised, can be changed.– Long experience in forensic environments.• Disadvantages:– High intra-class variability– Forgeries– Higher error rates than other traits– Affected by the physical and emotional state of the user– Large temporal variationPattern Recognition System(On-line Signature Verification)Pattern Recognition System• Acquisition devices:XYPAzAl0 100 200 300Pattern Recognition ProcessPreprocessing• For online signatures no segmentation needs to be performed– All parts of the signature are known after sensing• Attempt to eliminate noise from the capturing device, speed of writing, and the writing itself• Minimize the potential of eliminating writer dependencies• Solutions1) Size Normalization2) Position Normalization3) Smoothing4) Re-sampling5) Ligature-1500 -1000 -500 0 500 1000 1500 2000 2500-1500-1000-50005001000-500 0 500 1000 1500 2000 2500 3000 3500 4000-800-600-400-2000200400600800-2000 -1500 -1000 -500 0 500 1000 1500 2000-1000-500050010001500-2000 -1500 -1000 -500 0 500 1000 1500 2000 2500-800-600-400-2000200400600800• Position Normalization:Initial SampleCenter of MassResample• In order to compare two signatures with respect to their shape, they must be re-sampled to eliminate the dependencies on speed– Sample rate: 100 samples/second• Temporal features must be extracted beforehand since all local speed information is lost during this processResampling• Ensures that the signature is uniformly smoothed– Segments of high writing velocity will be smoothed more than segments that are written slowSmoothing∑−=+=σσ22*iorigitifilteredtxfx• A one dimensional Gaussian filter is used in both the x and y directions– Small changes in the signal are smoothed out while the overall structure is kept• Each segment between critical points is smoothed separately in order to retain their absolute positionswhere∑−=−−=σσσσ22222222jjiieefStroke Concatenation• A stroke is the points input between a pen down and pen up sequence• All strokes are connected into one long string– This is done in order to facilitate the use of the string matching procedureCritical Points• Def: points that carry more information than others– Endpoints of strokes– Points of trajectory changeCritical Points• Re-sampling or smoothing of these points will discard important information about the structure and speed of the signature– Accordingly, these points are never changed throughout preprocessing– The speed information is stored at each of these pointsPreprocessing Steps• Original• Critical pointsPreprocessing Steps• Fine re-sampling• Gaussian filterPreprocessing Steps• Coarse re-sampling• Stoke concatenationPattern Recognition ProcessFunctional vs. Parametric• Functional Approaches (local)– Complete signals (e.g. x(t), y(t), p(t), v(t), a(t), etc.) are considered as mathematical time functions whose values are directly correlated with the feature set.– Difficulties are encountered in the matching step (temporal differences and non-linear distortions)– Feature extracting is