1. © S. Cha8/8/2002CSIS Automatic Detection of Handwriting forgery Dr. Sung-Hyuk Cha & Dr. Charlies C. Tappert School of Computer Science & Information Systems

2. © S. Cha8/8/2002CSIS Analysis of Handwriting RecognitionExaminationPersonality identification (Graphology) On-lineOff-lineWriter VerificationWriter Identification Natural WritingForgeryDisguised Writing Handwriting Analysis Taxonomy

3. © S. Cha8/8/2002CSIS Background Differences b/w authentic handwriting & forgery Measure of Wrinkliness Automatic Forgery Detection Model ConclusionOverview

4. © S. Cha8/8/2002CSIS To determine the Validity of Individuality in Handwriting Legal Motivation Frye vs. US (1923) scientific community Daubert vs. Merrell Dow (1993) testing, peer review, error rates U.S. vs. Starzecpyzel (1995) “skilled” testimony GE vs. Joiner (1997) weight of evidence Kumho vs.Carmichael (1999) reliability standard

5. © S. Cha8/8/2002CSIS Each person writes differently. Individuality of Handwriting

6. © S. Cha8/8/2002CSIS (b) Forgeries of (a) (a) Authentic handwriting samples from one writer Authentic vs. Forgeries

7. © S. Cha8/8/2002CSIS 3 Differences b/w authentic & forgery 1. Shape 2. Pressure 3. Speed

8. © S. Cha8/8/2002CSIS Angular and Magnitude Type Element String AngularMagnitude ImageStroke DirectionStroke Width

9. © S. Cha8/8/2002CSIS w1w2w3w4w5w6w7 5365455 min(wi) = 3 w1 w2 w3 w4 w5 w6 w7w8w9w10 4.24 2.83 4.24 2.83 min(wi) = 2.83 Stroke Width Extraction (a) Vertical & horizontal stroke width(b) Diagonal stroke width

10. © S. Cha8/8/2002CSIS Fractal: How Long is a Coastline?

11. © S. Cha8/8/2002CSIS Fractal: How wrinkly is the Coastline of Britain?

12. © S. Cha8/8/2002CSIS (a) Number of in the boundary = 69 (b) Number of in the boundary = 32 (a) (b) Fractal: How wrinkly is Handwriting?

13. © S. Cha8/8/2002CSIS Fractal: Measure of Wrinkliness

14. © S. Cha8/8/2002CSIS (d-e) ascender & descender Computational Features

15. © S. Cha8/8/2002CSIS (f) stroke width (g-i) projected histogram and gradient histogram Computational Features

16. © S. Cha8/8/2002CSIS sample1 by xsample2 by xsample1 by xForgery of x by y Feature Extractor Distance computing d-dimensional within-authentic- handwriting distance set d-dimensional between-authentic- handwriting & forgery distance set Automatic Forgery Detection Model

17. © S. Cha8/8/2002CSIS.49.70.71.13.47.32.21.49.75.70.26.54.35.18.49.67.74.23.48.32.22.72.33.47.66.60.42.10.74.33.48.60.59.45.10.79.36.54.60.59.52.09.30.61.66.70.71.57.10.42.72.64.67.74.53.10.40.75.67.75.70.54.11.30.60.59.66.60.36.10.32.60.59.60.59.39.10.30.66.60.60.59.34.09 cent slant wid zone side-h bot-h grad Feature distances AAAAAAFFFFFFAAAAAAFFFFFF Truth Inputs & Truth

18. © S. Cha8/8/2002CSIS Original/ Forgery? Distance compu- tation Feature extraction Authentic sample from a known source Handwriting sample in question Artificial Neural Network

19. © S. Cha8/8/2002CSIS Distributions and Errors between authentic & forgery distance within authentic distance forgery identified authentic identified as forgery Decision boundary d (,),)

20. © S. Cha8/8/2002CSIS within class between class 60180 Random selection dichotomizer s’ -error d’ -error dichotomizer s -error d -error estimate Design of Experiment

21. © S. Cha8/8/2002CSIS Conclusion Authentic handwriting and forgery handwritten word images were collected. Differences b/w authentic handwriting and forgery Measure of Wrinkliness Automatic Forgery Detection Model using the dichotomy approach. Further quantitative study with more samples is necessary.

22. © S. Cha8/8/2002CSIS The End Thank you. http://www.csis.pace.edu/~scha/handwriting.html