Introduction to Online Signature Verification

1. CSE - 717 Introduction to Online Signature Verification Swapnil Khedekar

2. Signature Verification • Biometric • 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 • Behavioral biometrics • Verify user signatures using computers or embedded devices • Efficient and effective method of replacing insecure passwords, PIN numbers, keycards and ID cards

3. 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

4. Individuality • Physiology studies suggest • Handwriting originates & develops in brain • Signal to duplicate mental picture of character or word is sent 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, teacher’s style etc • Class: Conscious/unconscious individual changes • Axiom • A person is unlikely to ever duplicate any signature exactly

5. Difference • Dynamic/Online • 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 • Around 99% accuracy • Static/Offline • 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 • [Rigoll98] performed systematic comparison of online-offline techniques • & their performance. Concluded with preference for on-line verification system.

6. Capture Devices • Technology • 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 SignatureGem SigLite ClipGem ePad-ID

7. 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 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

8. Typical System • Reference signature: • Data acquisition • Pre-processing • Feature extraction • Matching • A distance metric criteria is assumed • Distance between test and reference signature is calculated • If distance < threshold, it is authenticated • Performance Evaluation • On skilled and random forgeries • No public standard signature dataset

9. 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

10. Distance Functions • Linear Discriminant function • 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 • Euclidian Distance Classifier • G(T) = (1/n) ∑ ( (ti – ri) / si )2 • Least distant value is compared with threshold • Synthetic Discriminant Matching • Mostly used as post-processor in combination • Finds filter impluse response w from samples • Proposed by [Wilkinson90] and [Bahri88]

11. Distance Functions • Dynamic Programming Matching • Minimize the residual error between two functions by finding a warping 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 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

12. 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 combining errors 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

13. 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 random forgeries • 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

14. 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

15. 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 won’t 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

16. Data transfer • Storage & Retrieval [Han97] • 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

17. Conclusion • 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