Online Signature Verification

1
Online Signature Verification

• Based on Dynamic Regression
• Signature Verification Group _at_Cedar 11/06/2003

2
Signature verification-gtBasic Procedure

• 1. Template generation
• In real application, the number of given genuine
  signatures is very few (usually less than 6) and
  no forgery is provided.
• 2. Matching based on the template.
• Input one signature, output a
  confidence(0-100) that the signature is genuine.

3
Signature verification-gt1. Template generation

• The challenges are
• 1).Very limited signatures for training.
• Usually we can not expect more than 6 genuine
  signatures for training for each subject. This is
  unlike handwriting recognition.
• 2). Decide the consistent features.
• There are over 100 features for signature2,
  such as Width, Height, Duration, Orientation, X
  positions, Y positions, Speed, Curvature,
  Pressure, so on.

4
Signature verification-gt1. Template generation

• We have following experience
• 1). The most reliable feature is the shape of
  the signature.
• 2). The second reliable feature is the speed of
  writing.
• 3). No other features are consistent.
• To represent shape and speed, each signature
  is a 3-D sequence SigiXi, Yi, Vi, where Vi
  is the sequence of speed magnitude. Then we use
  Dynamic Regression to match two signatures and
  return a Confidence of similarity (0-100).

5
Template Generation

• Features we choose
• Sequence of X Y

Genuine Sig.
X positions
Y positions
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Template Generation

• Features comparison

X from genuine sig.
X from forgery sig.
Genuine sig.
Forgery sig.
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Template Generation

• More features
• X, Y positions are not enough. We need spatial
  features that describe the shape of the signature
  curve. Torques, Curvature-ellipse are candidates

Torques of genuine sig.
Torques of forgery sig.
Now we can distinguish them !
8
Template Generation

• More features Curvature Ellipse

S1 of Curvature Ellipse (genuine)
S1 of Curvature Ellipse (forgery)
S2 of Curvature Ellipse (genuine)
S2 of Curvature Ellipse (forgery)
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Template Generation

• Curve Matching Segmentation

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Signature verification-gt2. Matching

• Traditional Simple Regression

Similarity 91
Similarity 31
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Signature verification-gt2. Matching

• Traditional Simple Regression
• Advantages Invariant to scale and
  translation Similarity (Goodness- of-fit)
  makes sense.
• Disadvantages One-one alignment, brittle.

One-One alignment
Dynamic alignment
12
Signature verification-gt2. Matching

• Dynamic Regression

( y2 is matched x2, x3, so we extend it to be two
points in Y sequence.)
The DTW warping path in the n-by-m matrix is the
path which has minimum average cumulative cost.
The unmarked area is the constrain that path is
allowed to go.
13
Signature verification-gtDemo System
Enroll two or more genuine signatures
14
Signature verification-gtDemo System
Verifying signature. Similarity is output and
Accept/Reject is recommended
15
Signature verification-gtRemarks

• Segmentation?
• Signature is an art of drawing, not limited to
  some kind language. Segments by Perceptually
  Important Points7 are by no means consistent
  during genuine signature of one subject.

16
Signature verification-gtRemarks

• User-dependent distance threshold?
• Distance (Euclidean, DTW, etc.) for similarity
  measure is so embarrassing. In real applications,
  users tends to ask how similar is the two
  signatures? Or, what is the confidence that this
  signature is genuine? It is nature and friendly
  to answer their similarity confidence is 90!
  (instead of saying their distance of
  dissimilarity is 5.8). Our demo system shows
  that the answer by Dynamic Regression really
  makes sense.

17
References

• 1 Rejean Plamondon, Guy Lorette. Automatic
  Signature Verification and Writer
  identification-the state of the art. Pattern
  Recognition, Vol.22, No.2, pp.107-131, 1989.
• 2 F. Leclerc and R. Plamondon. Automatic
  signature verification the state of the art
  1989-1993. International Journal of Pattern
  Recognition and Artificial Intelligence,
  8(3)643-660, 1994.
• 3 Luan L. Lee, Toby Berger, Erez Aviczer.
  Reliable On-line Human Signature Verifications
  Systems. IEEE trans. On Pattern Analysis and
  Machine Intelligence, Vol. 18, No.6, June 1996.
• 4 R. Plamondon. The Design of On-line Signature
  Verification System From Theory to Practice.
  Intl J. Pattern Recognition and Artificial
  Intelligence, vol. 8, no. 3, pp. 795-811, 1994.
• 5 Mario E. Munich, Pietro Perona. Visual
  Identification by Signature Tracking. IEEE Trans.
  On Pattern Analysis and Machine Intelligence,
  Vol. 25, No. 2, pp. 200-216, February 2003.

18
References

• 6 Vishvjit S. Nalwa. Automatic On-line
  Signature Verification. Proceedings of the IEEE,
  Vol. 85, No. 2, pp. 215-239, February 1997.
• 7 Jean-Jules Brault and Rejean Plamondon.
  Segmenting Hanwritten Signatures at Their
  Perceptually Important Points. IEEE Trans. On
  Pattern Analysis and Machine Intelligence, Vol,
  15, No. 9, pp. 953-957, September 1993.
• 8 Taik H. Rhee, Sung J. Cho, Jin H. Kim.
  On-line Signature Verification Using Model-Guided
  Segmentation and Discriminative Feature Selection
  for Skilled Forgeries. Sixth International
  Conference on Document Analysis and
• Recognition (ICDAR '01), September, Seattle,
  Washington, 2001.
• 9 Thomas B. Sebastian, Philip N. Klein, Bejamin
  B. Kimia. On Aligning Curves. IEEE Trans. On
  Pattern Analysis and Machine Intelligence, Vol.
  25, No. 1, January 2003.
• 10 A.K. Jain, Friederike D. Griess and Scott D.
  Connell. On-line Signature Verification. Pattern
  Recognition, vol. 35, no. 12, pp. 2963--2972, Dec
  2002.

19
References

• 11 K. Huang and H. Yan, On-Line Signature
  Verification Based on Dynamic segmentation and
  Global and Local Matching, Optical Eng., vol.
  34, no. 12, pp. 3480-3487, 1995.
• 12 G. Lorette and R. Plamondon, Dynamic
  Approaches to Hand-written Signature
  Verification, Computer Processing of
  Hand-writing, pp. 21-47, 1990.
• 13 R. Martens and L. Claesen, On-Line
  Signature Verification by Dynamic Time-Warping,
  Proc. 13th Intl Conf. Pattern Recognition, pp.
  38-42, 1996.
• 14 B. Wirtz, Stroke-Based Time Warping for
  Signature Verification, Proc. Intl Conf.
  Document Analysis and Recognition, pp. 179-182,
  1995.