Transcript Slide 1

CSE - 717
Introduction to
Online Signature Verification
Swapnil Khedekar
Signature Verification
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Biometric
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Technology that verifies a user's identity by
measuring a unique-to-the-individual biological trait
Creates trust by establishing a context of confident
privacy and undeniable personal responsibility
Future and destiny of computerized network
security and identification is Biometrics
Signature verification
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Behavioral biometrics
Verify user signatures using computers or
embedded devices
Efficient and effective method of replacing insecure
passwords, PIN numbers, keycards and ID cards
Why Signatures?
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Advantages
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Customary way of identity verification
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Even advanced PDAs focus pen-input
People are willing to accept a signature based verification
Easier, faster, low FRR, low memory
Disadvantages
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Dynamic Biometric, Non-repudiation
Can be forged easily
Individuality
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Physiology studies suggest
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Handwriting originates & develops in brain
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Signal to duplicate mental picture of character or word is
sent to the arm and hand
Handwriting system = Machine
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General or class characteristics
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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: Effect of culture, trend, teacher’s style etc
Class: Conscious/unconscious individual changes
Axiom
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A person is unlikely to ever duplicate any signature exactly
Difference
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Static/Offline
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Early 1970’s
Only image of signature
No need of special
hardware, ubiquitous use
Large storage
Can not trace speed, style,
pressure etc
Easier to forge
Around 95% accuracy
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Dynamic/Online
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Early 1990’s
Uses shape, speed, pressure
Needs special digital
surface, pads and pen etc.
Numeric data, small storage
Can use speed, pressure,
angle of pen etc to further
exploit individuality
Harder to forge
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Around 99% accuracy
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•[Rigoll98] performed systematic comparison of online-offline techniques
& their performance. Concluded with preference for on-line verification system.
Capture Devices
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Technology
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SignatureGem
Pressure sensitive sensors arranged in compact
grid to form flat surface
When pen touches a sensor, pressure at that
sensor is calculated
The sensors are scanned periodically for pen
positions
Position of sensor, pressure, pen angle are stored
Periodic scanning results in sequence of
parameters
SigLite
ClipGem
ePad-ID
Issues
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People use full names, initials or complex signs
People tend to vaguely write ending part, dots etc
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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 natural
writing can never be forged
Also suggested that samples should be collected
over time, not at single time
[Hilton92] claimed single-most important feature is
movement
Typical System
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Reference signature:
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Matching
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Data acquisition
Pre-processing
Feature extraction
A distance metric criteria is assumed
Distance between test and reference signature is
calculated
If distance < threshold, it is authenticated
Performance Evaluation
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On skilled and random forgeries
No public standard signature dataset
Features Used
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Features for online signatures
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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
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None related to shape
1% FRR, 20% FAR on timed forgeries
Distance Functions
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Linear Discriminant function
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Euclidian Distance Classifier
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Linear combination of features fi
G(x) = wtx + w0, w=weighing vector,w0=class const
Some researchers proposed feature vector
normalized by reference mean ri or std. deviation si
G(T) = (1/n) ∑ ( (ti – ri) / si )2
Least distant value is compared with threshold
Synthetic Discriminant Matching
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Mostly used as post-processor in combination
Finds filter impluse response w from samples
Proposed by [Wilkinson90] and [Bahri88]
Distance Functions
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Dynamic Programming Matching
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Minimize the residual error between two functions by finding a
warping function
Rescales one of original functions time axis
Majority Classifier
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Main drawback of previous techniques
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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]
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Creates a universal prototype for signature, new signature is
assigned a distance from the prototype
Uses 21 Global & 5 local features
Segmentation, parameter re-estimation done by the Viterbi
1% FRR, 2.5% FAR
Distance Functions
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Multi-expert System [DiLeece00]
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3 independent agents. Result by majority
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3.2% FRR, 0.55% FAR with 3.2% undecided
Velocity-based Models [Nalwa97]
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Shape-based features and holistic analysis
Speed-based features
Regional Analysis
Velocities are hard to copy, good forgery detectors
Look at both local and global models
Weighted and biased harmonic mean as a way of combining
errors from multiple models
2-5% error rate
Split-and-Merge [Lee97]
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Static and dynamic features, Polar coordinates
For Chinese signatures
Splits into 2 parts & evaluate each & then combines results
13% FAR, 3% FRR
Distance Functions
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Deformable structures [Pawlidis98]
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Neural networks [Paulik99]
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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
Illustrates the difference in error by skilled versus random
forgeries
Random : 0.25% FAR & FRR. Skilled:2.3% FAR & 7% FRR.
Curve aligning [Sebastian03]
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Compares the curves using an alignment curve
Edit distance on length and curvature for aligning
Alignment curve created a from prototype of each segment
Software products
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PenOp
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Sign-On
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For verifying static signatures on cheques
Cadix ID-007
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For online signature login
Dynamically updates reference signatures
2.5% FRR & FAR
Signer confidence
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Peripheral Vision
Use can login only using handwritten signatures
Online signature verification in less than 1 sec
CounterMatch
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Claims to match signature in any language
Software products
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Kappa
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ApproveIT
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Signature added to WordPerfect document directly from penbased input
If content of document are changed, signature won’t appear
Unipen
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Uses “user-specific” features for lower FRR
Tested on 8500 postal images. 0.85% FRR
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
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SignCrypt, Q-Lock, Cyber-Sign
Data transfer
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Storage & Retrieval [Han97]
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For Signature identification, can be extended for
verification
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 and
indexing these strings
Conclusion
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The new system should be an on-line system
Shape is an integral part of signature verification, it
is 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 strokes
needs 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