Online Signature as a Behavioral Biometric

1. Online Signature as a BehavioralBiometric Berrin Yanıkoğlu

2. Problem • Given a signature and a claimed identity, decide on whether to accept or reject the signature. • Signature is a behavioral biometric such as gait and voice, as opposed to physiological biometrics such as fingerprint and iris. • Advantages: • No need to remember pins or carry tokens/cards • Well accepted socially and legally • Already used in a number of applications (e.g., points of sales) • Acquisition hardware already integrated in devices (Tablet PC, PDA, …) • Changeable • Disadvantages: • May be forged

3. Signature Verification • Dynamic (online): • Pressure-sensitivetabletsusedtocapturetheimageanddynamicproperties of thesignature • Moreunique, moredifficulttoforge • Applications: creditcardprocessing, addedsecuritytolaptops/PDAs... • Static (offline) • Image of thesignature is theinput • Applications: bank checkclearing 3/8/2002 Dear John, ........... ....................... ................................... ................................... .................. Best regards,

4. x,y • Time Stamp • Pressure • Pen Inclination • Curvature • Acceleration Input Signature Verification - Jan 2010 - B. Yanikoglu

5. Sample Vector Representation • (x1 y1 p1 t1) • (x2 y2 p2 t2) • S = ... • (xN yN pN tN) where xi and yi are the coordinates and pi and ti are the pressure and timestamp at point i. There may be more features which are measured or extracted.

6. Genuine and Forgery Signatures

7. Genuine Signature Variations • Common variations in genuine signatures: • Size • Pen thickness • Extra/missing/longer/shorter strokes • Rotation • Relative position of strokes Easier to handle Difficult to handle

8. Genuine vs Forgeries Forgeries Genuine Main difficulty in signature verification: high intra-class and low inter-class variations

9. Genuine vs Forgeries • Reference set (all genuine): • Queries:

10. Genuine vs Forgeries Forgery • Reference set (all genuine): • Queries:

11. Genuine vs Forgeries Forgery • Reference set (all genuine): • Queries:

12. Genuine vs Forgeries Forgery Genuine • Reference set (all genuine): • Queries:

13. Genuine vs Forgeries Forgery Genuine • Reference set (all genuine): • Queries:

14. Genuine vs Forgeries Forgery Forgery Genuine • Reference set (all genuine): • Queries:

15. Genuine vs Forgeries Forgery Forgery Genuine • Reference set (all genuine): • Queries:

16. Genuine vs Forgeries Genuine Forgery Forgery Genuine • Reference set (all genuine): • Queries:

17. Matching and Verification

18. Matching/Verification • Needa similarity/distancemeasure. • signatures are of varyinglength • distance measure should be insensitivetointra-classvariations in shapeortiming • Thenwe can accept a signature as genuineifthedistance is small; rejectotherwise x x

19. Euclidian distance? • Dynamic Time Warping

20. Fourier Coefficients in Online Signature Verification 7.5% EER in SUSIG database

21. Local Features • Spatial features (at each trajectory point): • x, y coordinates w.r.t the center of the signature • x & y offset btw. two consecutive points • sine & cosine of the angle with x axis • curvature • grey values in the 9x9 neighbourhood • ... • Dynamic features (at each trajectory point): • absolute speed: (pi – pi-1)/ (ti – ti-1) distance per sample pts • relative speed (absolute speed normalized by the average signing speed) acceleration • pressure • pen tilt • ...

22. Dynamic Time Warping A Dynamic Programming Approach

23. Dynamic time warping • Goal:find the best non-linear alignment between two sequences, such that the total alignment cost is minimized. • Method: Dynamic time warping is an algorithm for measuring similarity between two sequences which may vary in time/speed/length.

24. Dynamic Programming • Dynamic Time Warping used here is a special case of Dynamic Programming which is • a powerful optimization technique for certain problems • where there are overlapping subproblems and optimal structure. • Dynamic Programming uses the solutions to subproblems in the globally optimal solution. • Many application areas: • String edit distance • Viterbi algorithm in Hidden Markov Models • Longest common subsequence • ...

25. For the following sequences: S1: ABCDFGGGXYZ S2:ABCDDFFGXYZ what is the `best` alignment? Depend on costs associated with insertion, deletion and substitution. ABCD.F. GGGXYZ ABCDDFFG . . XYZ 4 insertion/deletions ABCDFGGGXYZ ABCDDFFGXYZ 3 substitutions Notice how different paths on the grid correspond to different alignments. There are exponentially many paths and enumerating each of them to compute the cost is infeasible! This is where Dynamic Programming principle come into play. Dynamic Programming Example: Sequence Alignment ...

26. Dynamic Programming Example: Sequence Alignment N,M F F D D D C B A We need to fill the cost matrix on the right, according to the formulas below and keep a back pointer to the cell that gave the minimum value: Cost[0,0]=0 Cost[i,0]=i*InsertionCost Cost[0,j]=j*DeletionCost 0,0 A B C D E FG G G Euclidian dist.

27. Dynamic Programming Example: Sequence Alignment N,M F F D D D C B A Note that: 1) Cost[i,j] stores the minimum cost of all the paths starting from (0,0) and ending at (i,j). 2) Cost[i,j]is independent from the rest of the alignment (from i,j to N,M). Hence, we can compute it once for all the alternative paths! 0,0 A B C D E FG G G 3) We won`t know until the end whether the optimal alignment (global solution) passes through i,j or any other grid locations.

28. Dynamic time warping for Online Signatures • Signaturealignment is basically done thesame as sequencealignment: • instead of letters at eachindex, wecomparelocalfeaturessuchas normalized x,ycoordinates • decidewhether an insertion, deletionormatchingwithpenalty (diagonal move) wouldresult in a minimum cost • we typically use the same cost for insertion and deletion, but they should be adjusted compared to the matching cost such that the path does not always degenerate to a sequence of Delete followed by Inserts.

29. Common (EER) Threshold User-based score distribution of genuine and forgery users

30. Common (EER) Threshold • User-based score distribution of genuine and forgery users • Especially applicable for voice, signature ... biometrics • Need for user-based score normalization • There is a lot of research in this area.

31. Verification

32. Y • Calculated Distances: • Maximum Distance to Ref. Set • Minimum Distance to Ref. Set • Distance to Template Sig. • … dmin x2 dtemplate xTemplate x3 x1 dmax x5 x4 Verification • Comparethe test signature(Y) tothereferencesignatures (Xi) belongingtotheclaimedidentity, obtaining: Y Typically, the distance to the nearest reference signature or the distance to a template signature, is used and are both reasonable choices.

33. Our System’s Performance • 2013 First Place in SigWiComp2013 • All tasks: Online (Japanese signatures) and Offline (Japanese and Dutch signatures) • 2011 1st place in ESRA2011 Online Signature Evaluation Campaign on Task1-DS3 • 2011 Winner of the SigComp2011 Offline Signature Verification (on Chinese database; 3rd place in Dutch database) • 2010 2010 2004 2003 1998 • Winner of the 4NSIGCOMP2010 Forensic Signature Verification • Winner of the First International Signature Verification Competition (SVC 2004) • Interpro Computing Awards – R&D Prize Finalist (Online Signature Verification System)