Signature Verification

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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

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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 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, teachers
  style etc
• Class Conscious/unconscious individual changes
• Axiom
• A person is unlikely to ever duplicate any
  signature exactly

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Difference

• Dynamic/Online
• Early 1990s
• 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 1970s
• 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.

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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
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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
  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

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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 is
  calculated
• If distance lt 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, wweighing vector,w0class 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

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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 -gt 100 as FRR -gt 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

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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
  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

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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 random forgeries
• Random 0.25 FAR FRR. Skilled2.3 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 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

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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