http://iet.metastore.ingenta.com
1887

UTSig: A Persian offline signature dataset

UTSig: A Persian offline signature dataset

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The pivotal role of datasets in signature verification systems motivates researchers to collect signature samples. Distinct characteristics of Persian signature demand for richer and culture-dependent offline signature datasets. This study introduces a new and public Persian offline signature dataset, UTSig (University of Tehran Persian Signature), that consists of 8280 images from 115 classes. Each class has 27 genuine signatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6 forgers. Compared with the other public datasets, UTSig has more samples, more classes, and more forgers. The authors considered various variables including signing period, writing instrument, signature box size, and number of observable samples for forgers in the data collection procedure. By careful examination of main characteristics of offline signature datasets, they observe that Persian signatures have fewer numbers of branch points and end points. They propose and evaluate four different training and test setups for UTSig. Results of the authors’ experiments show that training genuine samples along with opposite-hand samples and random forgeries can improve the performance in terms of equal error rate and minimum cost of log likelihood ratio.

References

    1. 1)
      • 1. Yeung, D.-Y., Chang, H., Xiong, Y., et al: ‘SVC2004: first international signature verification competition’, in Zhang, D., Jain, A.K. (Eds.): ‘Biometric authentication’ (Springer, 2004), pp. 1622.
    2. 2)
      • 2. Blankers, V.L., Heuvel, C., Franke, K.Y., et al: ‘Icdar 2009 signature verification competition’. 10th Int. Conf. on Document Analysis and Recognition, 2009, ICDAR'09, 2009, pp. 14031407.
    3. 3)
      • 3. Liwicki, M., Malik, M.I., van den Heuvel, C.E., et al: ‘Signature verification competition for online and offline skilled forgeries (sigcomp2011)’. Int. Conf. on Document Analysis and Recognition (ICDAR), 2011, 2011, pp. 14801484.
    4. 4)
      • 4. 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.
    5. 5)
      • 5. Malik, M.I., Liwicki, M., Alewijnse, L., et al: ‘ICDAR 2013 competitions on signature verification and writer identification for on- and offline skilled forgeries (SigWiComp 2013)’. 12th Int. Conf. on Document Analysis and Recognition (ICDAR), 2013, 2013, pp. 14771483.
    6. 6)
      • 6. 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.
    7. 7)
      • 7. Liwicki, M., Van Den, H.C.E., Found, B., et al: ‘Forensic signature verification competition 4NSigComp2010 – detection of simulated and disguised signatures’. 12th Int. Conf. on Frontiers In Handwriting Recognition, 2010, 2010, pp. 715720.
    8. 8)
      • 8. Plamondon, R., Lorette, G.: ‘Automatic signature verification and writer identification – the state of the art’, Pattern Recognit., 1989, 22, (2), pp. 107131.
    9. 9)
      • 9. Guo, J.K., Doermann, D., Rosenfield, A.: ‘Off-line skilled forgery detection using stroke and sub-stroke properties’. Proc. of 15th Int. Conf. on Pattern Recognition, 2000, 2000, pp. 355358.
    10. 10)
      • 10. Mitra, A., Banerjee, P.K., Ardil, C.: ‘Automatic authentication of handwritten documents via low density pixel measurements’, Int. J. Comput. Intell., 2005, 2, (4), pp. 219223.
    11. 11)
      • 11. Vargas, F., Ferrer, M.M.A., Travieso, C.C.M., et al: ‘Off-line handwritten signature GPDS-960 Corpus’. ICDAR, 2007, pp. 764768.
    12. 12)
      • 12. Plamondon, R., Srihari, S.N.: ‘Online and off-line handwriting recognition: a comprehensive survey’, IEEE Trans.Pattern Anal. Mach. Intell., 2000, 22, (1), pp. 6384.
    13. 13)
      • 13. Leclerc, F., Plamondon, R.: ‘Automatic signature verification: the state of the art – 1989-1993’, Int. J. Pattern Recognit. Artif. Intell., 1994, 8, (03), pp. 643660.
    14. 14)
      • 14. Pal, S., Blumenstein, M., Pal, U.: ‘Off-line signature verification systems: a survey’. Proc. of the Int. Conf. & Workshop on Emerging Trends in Technology, 2011, pp. 652657.
    15. 15)
      • 15. Pal, S., Blumenstein, M., Pal, U.: ‘Non-English and non-Latin signature verification systems a survey’. AFHA, 2011, pp. 15.
    16. 16)
      • 16. Chalechale, A., Mertins, A.: ‘Line segment distribution of sketches for Persian signature recognition’. Conf. on Convergent Technologies for the Asia-Pacific Region, TENCON 2003, 2003, pp. 1115.
    17. 17)
      • 17. Fiérrez-Aguilar, J., Alonso-Hermira, N., Moreno-Marquez, G., et al: ‘An off-line signature verification system based on fusion of local and global information’, in Maltoni, D., Jain, A.K. (Eds.): ‘Biometric authentication’ (Springer, 2004), pp. 295306.
    18. 18)
      • 18. Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., et al: ‘MCYT baseline corpus: a bimodal biometric database’, IEE Proc., Vis. Image Signal Process., 2003, 150, (6), pp. 395401.
    19. 19)
      • 19. Ferrer, M.a, Alonso, J.B., Travieso, C.M.: ‘Offline geometric parameters for automatic signature verification using fixed-point arithmetic’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (6), pp. 993997.
    20. 20)
      • 20. Franke, K., Schomaker, L., Veenhuis, C., et al: ‘WANDA: a generic framework applied in forensic handwriting analysis and writer identification’. Proc. of 3rd Int. Conf. on Hybrid Intelligent Systems, Design and Application of Hybrid Intelligent Systems , 2003, pp. 927938.
    21. 21)
      • 21. Pourshahabi, M.R., Sigari, M.H., Pourreza, H.R.: ‘Offline handwritten signature identification and verification using contourlet transform’. 2009 Int. Conf. of Soft Computing and Pattern Recognition, 2009, pp. 670673.
    22. 22)
      • 22. Liwicki, M., Malik, M.I., Alewijnse, L., et al: ‘ICFHR 2012 competition on automatic forensic signature verification (4NsigComp 2012)’. Int. Conf. on Frontiers in Handwriting Recognition (ICFHR), 2012, 2012, pp. 823828.
    23. 23)
      • 23. Johnson, E., Guest, R.: ‘The use of static biometric signature data from public service forms’, in Vielhauer, C., Dittmann, J., Drygajlo, A., et al (Eds.): ‘Biometrics and ID management’ (Springer, 2011), pp. 7382.
    24. 24)
      • 24. Srihari, S.N., Xu, A., Kalera, M.K.: ‘Learning strategies and classification methods for off-line signature verification’. Ninth Int. Workshop on Frontiers in Handwriting Recognition, 2004, IWFHR-9 2004’, 2004, pp. 161166.
    25. 25)
      • 25. Bertolini, D., Oliveira, L.S.L.S., Justino, E., et al: ‘Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers’, Pattern Recognit., 2010, 43, (1), pp. 387396.
    26. 26)
      • 26. Vargas, J.F., Ferrer, M.A., Travieso, C.M., et al: ‘Off-line signature verification based on grey level information using texture features’, Pattern Recognit., 2011, 44, (2), pp. 375385.
    27. 27)
      • 27. Guerbai, Y., Chibani, Y., Hadjadji, B.: ‘The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters’, Pattern Recognit., 2015, 48, (1), pp. 103113.
    28. 28)
      • 28. Brümmer, N., du Preez, J.: ‘Application-independent evaluation of speaker detection’, Comput. Speech Lang., 2006, 20, (2), pp. 230275.
    29. 29)
      • 29. Pal, S., Umapada Pal, M.B.: ‘A two-stage approach for English and Hindi off-line signature verification’, in Petrosino, A., Maddalena, L., Pala, P. (Eds.): ‘New trends in image analysis and processing – ICIAP 2013’ (Springer, Berlin Heidelberg, 2013), pp. 140148.
    30. 30)
      • 30. Pal, S., Blumenstein, M., Pal, U., et al: ‘Multi-script off-line signature verification: a two stage approach’. AFHA, 2013, pp. 3135.
    31. 31)
      • 31. Gonzalez Rafael, C., Woods Richard, E.: ‘Digital image processing third edition’ (Prentice-Hall, 2007).
    32. 32)
      • 32. Cortes, C., Vapnik, V.: ‘Support-vector networks’, Mach. Learn., 1995, 20, (3), pp. 273297.
    33. 33)
      • 33. Platt, J.C.: ‘Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods’, Adv. Large Margin Classif., 1999, 10, (3), pp. 6174.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2015.0058
Loading

Related content

content/journals/10.1049/iet-bmt.2015.0058
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address