access icon free Orthogonal function representation for online signature verification: which features should be looked at?

In this study, several feature combinations are studied to analyse their relevance for online signature verification. Different time functions associated with the signing process are analysed in order to provide some insight on their actual discriminative power. This analysis could also help forensic handwriting experts (FHEs) to further understand the signatures and the writer's behaviour. Among the different feature combinations analysed, a set of features which seems to be relevant for signature analysis by FHEs is particularly considered. The feasibility of developing a system which could complement the FHEs work is evaluated. Two different approximations of the analysed time functions are proposed, one based on the Legendre polynomials and another based on the wavelet decomposition. The coefficients in these orthogonal series expansions of the time functions are used as features to model them. Two different signature styles are considered, namely, Western and Chinese, of one of the most recent publicly available signature databases. The experimental results are promising, in particular for the features that seem to be relevant for the FHEs, since the obtained verification error rates are comparable with the ones reported in the state-of-the-art over the same datasets.

Inspec keywords: Legendre polynomials; wavelet transforms; behavioural sciences; handwriting recognition; approximation theory; database management systems; digital forensics

Other keywords: online signature verification; discriminative power; writer behaviour; time function analysis; forensic handwriting experts; Chinese signature styles; wavelet decomposition; signature analysis; verification error rates; FHE; signing process; Western signature styles; orthogonal series expansions; orthogonal function representation; Legendre polynomials

Subjects: Data security; Computer vision and image processing techniques; Database management systems (DBMS); Interpolation and function approximation (numerical analysis); Integral transforms in numerical analysis

References

    1. 1)
      • 18. Pal, S., Blumenstein, M., Pal, U.: ‘Non-English and Non-Latin signature verification systems: A survey’. Proc. First Int. Workshop on Automated Forensic Handwriting Analysis, Beijing, China, 2011.
    2. 2)
      • 31. Oliveira, L.S., Santos, C.R., Bortolozzi, F., Justino, E.: ‘Off-line signature verification based on forensic questioned document examination approach’. Proc. Symp. Applied Computing, Seoul, Korea, March 2007.
    3. 3)
      • 9. Maramatsu, D., Matsumoto, T.: ‘Effectiveness of pen pressure, azimuth, and altitude features for online signature verification’. Proc. Int. Conf. Biometrics, 2007, pp. 503512.
    4. 4)
      • 32. Pervouchine, V., Leedham, G.: ‘Extraction and analysis of forensic document examiner features used for writer identification’, Pattern Recognit., 2007, 40, (3), pp. 10041013 (doi: 10.1016/j.patcog.2006.08.008).
    5. 5)
      • 39. Xu, H., Veldhuis, R.N.J., Kevenaar, T.A.M., Akkermans, A.H.M., Bazen, A.M.: ‘Spectral minutiae: a fixed-length representation of a minutiae set’. Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition Workshops, 2008.
    6. 6)
      • 44. Breiman, L.: ‘Random forests’. Technical report, Department of Statistics, University of California, Berkeley, 2001.
    7. 7)
      • 23. Ueda, K.: ‘Investigation of off-line Japanese signature verification using a pattern matching’. Proc. Seventh Int. Conf. Document Analysis and Recognition, Edinburgh, Scotland, 2003.
    8. 8)
      • 15. Galbally, J., Fierrez, J., Martinez-Diaz, M., Plamondon, R.: ‘Quality analysis of dynamic signature based on the sigma-lognormal model’. Proc. 11th Int. Conf. Document Analysis and Recognition, Beijing, China, September 2011, pp. 633637.
    9. 9)
      • 8. Lei, H., Govindaraju, V.: ‘A comparative study on the consistency of features in on-line signature verification’, Pattern Recognit. Lett., 2005, 26, pp. 24832489 (doi: 10.1016/j.patrec.2005.05.005).
    10. 10)
      • 5. Impedovo, D., Pirlo, G., Plamondon, R.: ‘Handwritten signature verification: new advancements and open issues’. Proc. 13th Int. Conf. Frontiers in Handwriting Recognition, Bari, Italy, September 2012, pp. 367372.
    11. 11)
      • 22. ICDAR 2011: Int. Conf.Document Analysis and Recognition, Beijing, China, 19–21 September, 2011.
    12. 12)
      • 28. Will, E.J.: ‘Inferring relative speed of handwriting from the static trace’, J. Forensic Doc. Examination, 2012, 22, pp. 5562.
    13. 13)
      • 38. Tuyls, P., Akkermans, A.H.M., Kevenaar, T.A.M., Schrijen, G.-J., Bazen, A.M., Veldhuis, R.N.J.: ‘Practical biometric authentication with template protection’. Fifth Int. Conf. Audio- and Video-Based Personal Authentication (AVBPA), volume 3546 of Lect. Notes in Comput. Sci., Springer-Verlag, Berlin, 2005, pp. 436446.
    14. 14)
      • 1. Plamondon, R., Lorette, G.: ‘Automatic signature verification and writer identification – the state of the art’, Pattern Recognit., 1989, 22, (2), pp. 107131 (doi: 10.1016/0031-3203(89)90059-9).
    15. 15)
      • 2. Leclerc, F., Plamondon, R.: ‘Automatic signature verification: the state of the art–1989–1993’, Int. J. Pattern Recognit. Artif. Intell., 1994, 8, (3), pp. 643660 (doi: 10.1142/S0218001494000346).
    16. 16)
      • 27. Ballantyne, K.N., Bird, C.L., Found, B., Rogers, D.: ‘Dynamic features of naturally written, disguised and forged handwritten text’. Proc. 15th Conf. Int. Graphonomics Society., Cancun, Mexico, June 2011.
    17. 17)
      • 34. Jain, A.K., Griess, F.D., Connell, S.D.: ‘On-line signature verification’, Pattern Recognit., 2002, 35, pp. 29632972 (doi: 10.1016/S0031-3203(01)00240-0).
    18. 18)
      • 45. Brümmer, N., du Preez, J.: ‘Application-independent evaluation of speaker detection’, Comput. Speech Lang., 2006, 20, pp. 230275 (doi: 10.1016/j.csl.2005.08.001).
    19. 19)
      • 7. Kholmatov, A., Yanikoglu, B.: ‘Identity authentication using improved online signature verification method’, Pattern Recognit. Lett., 2005, 26, pp. 24002408 (doi: 10.1016/j.patrec.2005.04.017).
    20. 20)
      • 19. Lv, H., Wang, W., Wang, C., Zhuo, Q.: ‘Off-line chinese signature verification based on support vector machines’, Pattern Recognit. Lett., 2005, 26, pp. 23902399 (doi: 10.1016/j.patrec.2005.04.013).
    21. 21)
      • 17. Liwicki, M., Malik, M.I., den Heuvel, C.E., et al: ‘Signature verification competition for online and offline skilled forgeries (SigComp2011)’. Proc. 11th Int. Conf. Document Analysis and Recognition, Beijing, China, September 2011.
    22. 22)
      • 21. Zou, M., Tong, J., Liu, C., Lou, Z.: ‘On-line signature verification using local shape analysis’. Proc. 7th Int. Conf. Document Analysis and Recognition, Edinburgh, Scotland, UK, 2003.
    23. 23)
      • 29. Caligiuri, M., Mohammed, L.: ‘The neuroscience of handwriting’ (CRC Press, Florida, 2012).
    24. 24)
      • 12. Parodi, M., Gómez, J.C., Liwicki, M.: ‘Online signature verification based on legendre series representation. Robustness assessment of different feature combinations’. Proc. 13th Int. Conf. Frontiers in Handwriting Recognition, Bari, Italy, September 2012, pp. 377382.
    25. 25)
      • 13. Houmani, N., Mayoue, A., Garcia-Salicetti, S., et al: ‘Biosecure signature evaluation campaign (BSEC'2009): evaluating online signature algorithms depending on the quality of signatures’, Pattern Recognit., 2012, 45, (3), pp. 9931003 (doi: 10.1016/j.patcog.2011.08.008).
    26. 26)
      • 42. Chang, H., Dai, D., Wang, P., Xu, Y., Si, F., Huang, S.: ‘Online signature verification using wavelet transform of feature function’, J. Inf. Comput. Sci., 2012, 9, (11), pp. 31353142.
    27. 27)
      • 16. Blankers, V.L., van den Heuvel, C.E., Franke, K.Y., Vuurpijl, L.G.: ‘The ICDAR 2009 signature verification competition’. Proc. Tenth Int. Conf. Document Analysis and Recognition, Barcelona, Spain, July 2009, pp. 14031407.
    28. 28)
      • 4. Impedovo, D., Pirlo, G.: ‘Automatic signature verification: the state of the art’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2008, 38, (5), pp. 609635 (doi: 10.1109/TSMCC.2008.923866).
    29. 29)
      • 33. Fierrez-Aguilar, J., Ortega-Garcia, J., Ramos-Castro, D., Gonzalez-Rodriguez, J.: ‘HMM-based on-line signature verification: Feature extraction and signature modelling’, Pattern Recognit. Lett., 2007, 28, pp. 23252334 (doi: 10.1016/j.patrec.2007.07.012).
    30. 30)
      • 40. Golubitsky, O., Watt, S.M.: ‘Distance-based classification of handwritten symbols’, Int. J. Doc. Anal. Recognit., 2010, 13, (2), pp. 133146 (doi: 10.1007/s10032-009-0107-7).
    31. 31)
      • 3. Plamondon, R., Srihari, S.N.: ‘On-line and off-line handwriting recognition: a comprehensive survey’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (1), pp. 6384 (doi: 10.1109/34.824821).
    32. 32)
      • 26. Pal, S., Blumenstein, M., Pal, U.: ‘Hindi off-line signature verification’. Proc. 13th Int. Conf. Frontiers in Handwriting Recognit., Bari, Italy, September 2012, pp. 373378.
    33. 33)
      • 41. Daubechies, I.: ‘Ten lectures on wavelets’ (SIAM: Society for Industrial and Applied Mathematics, Pennsylvania, 1992).
    34. 34)
      • 25. Ismail, M.A., Gad, S.: ‘Off-line Arabic signature recognition and verification’, Pattern Recognit., 2000, 33, (10), pp. 17271740 (doi: 10.1016/S0031-3203(99)00047-3).
    35. 35)
      • 35. Freire, M.R., Martinez-Diaz, M., Fierrez, J., Ortega-Garcia, J.: ‘On the effects of sampling rate and interpolation in HMM-based dynamic signature verification’. Proc. Ninth Int. Conf. Document Analysis and Recognition, Curitiba, Brazil, 2007, pp. 11131117.
    36. 36)
      • 37. Ramos-Castro, D., Gonzalez-Rodriguez, J., Fierrez-Aguilar, J., Ortega-Garcia, J.: ‘Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems’, Forensic Sci. Int., 2005, 155, pp. 126140 (doi: 10.1016/j.forsciint.2004.11.007).
    37. 37)
      • 30. O'Reilly, C., Plamondon, R.: ‘Agonistic and antagonistic interaction in speed/accuracy tradeoff: a delta-lognormal perspective’, Hum. Mov. Sci., 2013, doi:10.1016/j.humov.2012.07.005.
    38. 38)
      • 14. Houmani, N., Garcia-Salicetti, S., Dorizzi, B.: ‘On assessing the robustness of pen coordinates, pen pressure and pen inclination to time variability with personal entropy’. Proc. IEEE Third Int. Conf. Biometrics: Theory, Applications, and Systems, Washington DC, USA, 2009.
    39. 39)
      • 24. Yoshimura, M., Yoshimura, I.: ‘Investigation of a verification system for Japanese countersignatures on traveler's cheques’. Trans. IEICE, 1998, J80-D-II, (7), pp. 17641773.
    40. 40)
      • 36. Yanikoglu, B., Kholmatov, A.: ‘Online signature verification using fourier descriptors’, EURASIP J. Adv. Signal Process., 2009, pp. 230275.
    41. 41)
      • 11. Richiardi, J., Ketabdar, H., Drygajlo, A.: ‘Local and global feature selection for on-line signature verification’. Proc. Eighth Int. Conf. Document Analysis and Recognition, Seoul, Korea, 2005.
    42. 42)
      • 43. SigComp2011. ICDAR 2011 Signature verification competition. Available at http://www.iapr-tc11.org/mediawiki/index.php/Datasets_List, 2011.
    43. 43)
      • 20. Ji, J.-W., Chen, X.-S.: ‘Off-line Chinese signature verification segmentation and feature extraction’. Proc. Int. Conf. Computational Intelligence and Software Engineering, 2009, pp. 14.
    44. 44)
      • 6. Yeung, D.-Y., Chang, H., Xiong, Y., et al: ‘SVC2004: first international signature verification competition’. Proc. Int. Conf. Biometric Authentication, Hong Kong, 2004, pp. 1622.
    45. 45)
      • 10. Pascual-Gaspar, J.M., Faundez-Zanuy, M., Vivaracho, C.: ‘Fast on-line signature recognition based on VQ with time modeling’, Eng. Appl. Artif. Intell., 2011, 24, (2), pp. 368377 (doi: 10.1016/j.engappai.2010.10.015).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2013.0025
Loading

Related content

content/journals/10.1049/iet-bmt.2013.0025
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading