SignatureVerification_2

8/13/2019 SignatureVerification_2

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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

Documents

SignatureVerification_2