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Introduction to
Online ign tureVerificationSwapnil Khedekar
CSE - 717
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Signature Verification
Biometric
Technology that verifies a user's identity bymeasuring a unique-to-the-individual biological trait
Creates trust by establishing a context of confidentprivacy and undeniable personal responsibility
Future and destiny of computerized networksecurity and identification is Biometrics
Signature verification
Behavioral biometrics Verify user signatures using computers or
embedded devices
Efficient and effective method of replacing insecure
passwords, PIN numbers, keycards and ID cards
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Why Signatures?
Advantages
Customary way of identity verification Even advanced PDAs focus pen-input
People are willing to accept a signature based verification
Easier, faster, low FRR, low memory
Disadvantages
Dynamic Biometric, Non-repudiation
Can be forged easily
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Individuality
Physiology studies suggest Handwriting originates & develops in brain
Signal to duplicate mental picture of character or word issent to the arm and hand
Handwriting system = Machine
Shoulder, arm, hand, fingers work as levers and fulcrums
During learning, signals are sent back to brain Strength & flexability of muscles, position of pen-grip and
the overall posture of the writer all affect the output
Mental state, writing instrument, surface etc also affect
Thus, each person has a small range of natural variation
General or class characteristics General: Effect of culture, trend, teachers style etc
Class: Conscious/unconscious individual changes
Axiom A person is unlikely to ever duplicate any signature exactly
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Difference
Static/Offline
Early 1970s
Only image of signature
No need of specialhardware, ubiquitous use
Large storage
Can not trace speed, style,pressure etc
Easier to forge Around 95% accuracy
Dynamic/Online
Early 1990s Uses shape, speed, pressure
Needs special digitalsurface, pads and pen etc.
Numeric data, small storage Can use speed, pressure,
angle of pen etc to furtherexploit individuality
Harder to forge
Around 99% accuracy
[Rigoll98] performed systematic comparison of online-offline techniques
& their performance. Concluded with preference for on-line verification system.
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Capture Devices
Technology
Pressure sensitive sensors arranged in compactgrid to form flat surface
When pen touches a sensor, pressure at thatsensor is calculated
The sensors are scanned periodically for pen
positions Position of sensor, pressure, pen angle are stored
Periodic scanning results in sequence ofparameters
SignatureGem SigLite ClipGem ePad-ID
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Issues
People use full names, initials or complex signs
People tend to vaguely write ending part, dots etc Signatures on bank cheques & delivery books
[Herbst99] showed trained experts can have 0%FAR, 25% FRR. Untrained have upto 50% FAR.
[Osborn29] claimed many characteristics of naturalwriting can never be forged
Also suggested that samples should be collectedover time, not at single time
[Hilton92] claimed single-most important feature ismovement
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Typical System
Reference signature:
Data acquisition
Pre-processing
Feature extraction
Matching A distance metric criteria is assumed
Distance between test and reference signature iscalculated
If distance < threshold, it is authenticated Performance Evaluation
On skilled and random forgeries
No public standard signature dataset
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Features Used
Features for online signatures
Total time
Signature path length
Path tangent angles
Signature velocity
Signature accelerations
Pen-up times & durations
[Crane83] proposed 44 while [Parks85]
proposed 90 features [Lee96] used 15 static & 34 dynamic
None related to shape
1% FRR, 20% FAR on timed forgeries
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Distance Functions
Linear Discriminant function
Linear combination of features fi
G(x) = wtx+ w0, w=weighing vector,w0=class const
Some researchers proposed feature vectornormalized by reference mean rior std. deviation si
Euclidian Distance Classifier
G(T) = (1/n) ( (tiri) / si)2
Least distant value is compared with threshold
Synthetic Discriminant Matching Mostly used as post-processor in combination
Finds filter impluse response wfrom samples
Proposed by [Wilkinson90] and [Bahri88]
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Distance Functions
Dynamic Programming Matching
Minimize the residual error between two functions by finding awarping function
Rescales one of original functions time axis
Majority Classifier
Main drawback of previous techniques FAR -> 100% as FRR -> 0% & vice versa
Single distant feature influences other close features
Genuine if atleast half features pass test
Hidden Markov Models [Kashi98]
Creates a universal prototype for signature, new signature isassigned a distance from the prototype
Uses 21 Global & 5 local features
Segmentation, parameter re-estimation done by the Viterbi
1% FRR, 2.5% FAR
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Distance Functions
Multi-expert System [DiLeece00]
3 independent agents. Result by majority Shape-based features and holistic analysis
Speed-based features
Regional Analysis
3.2% FRR, 0.55% FAR with 3.2% undecided
Velocity-based Models [Nalwa97] Velocities are hard to copy, good forgery detectors
Look at both local and global models
Weighted and biased harmonic mean as a way of combiningerrors from multiple models
2-5% error rate Split-and-Merge [Lee97]
Static and dynamic features, Polar coordinates
For Chinese signatures
Splits into 2 parts & evaluate each & then combines results
13% FAR, 3% FRR
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Distance Functions
Deformable structures [Pawlidis98]
Signature identification instead of signature verification Focus on an active vision system
Only orientation normalization, no size
Attempt to create a vague outline to classify easily
2.8% false recognition. But 18.3% inconclusive
Neural networks [Paulik99]
Illustrates the difference in error by skilled versus randomforgeries
Random : 0.25% FAR & FRR. Skilled:2.3% FAR & 7% FRR.
Curve aligning [Sebastian03]
Compares the curves using an alignment curve
Edit distance on length and curvature for aligning
Alignment curve created a from prototype of each segment
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Software products
PenOp Peripheral Vision Use can login only using handwritten signatures
Sign-On For online signature login
Dynamically updates reference signatures 2.5% FRR & FAR
Signer confidence For verifying static signatures on cheques
Cadix ID-007 Online signature verification in less than 1 sec
CounterMatch
Claims to match signature in any language
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Software products
Kappa
Uses user-specific features for lower FRR Tested on 8500 postal images. 0.85% FRR
ApproveIT
Signature added to WordPerfect document directly from pen-
based input If content of document are changed, signature wont appear
Unipen
Look for regularities and lawfulness in writing
Groups strokes together on a self-associating graph
Looks at predecessor and successor strokes
More similar to Handwriting Recognition
Others
SignCrypt, Q-Lock, Cyber-Sign
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Data transfer
Storage & Retrieval [Han97]
For Signature identification, can be extended forverification
Codes features of the signature into a string
Enters into database based on a hash-code of string
Loops end, branch, convex, concave points used
Proposed fast and efficient way of comparing andindexing these strings
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Conclusion
The new system should be an on-line system
Shape is an integral part of signature verification, itis a metric that is most easily imitated by a forger
Both global & local features should be used
Different methods have been tried with varying
results, About 99% at the best Great deal of speed improvement to be done
Signature segmentation into individual strokesneeds attention
Multi-expert system to integrate different methods Analysis on proper setting of thresholds & use of
user-specific thresholds
Sensors have developed to a fair point of saturation
Study on multi-lingual signatures is unfocused
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