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3D Research

, 9:48 | Cite as

Offline Handwritten Signature Verification Using Cylindrical Shape Context

  • Pradeep N. Narwade
  • Rajendra R. Sawant
  • Sanjiv V. Bonde
3DR Express
  • 46 Downloads
Part of the following topical collections:
  1. Object detection and Recognition

Abstract

Offline handwritten signatures is a convincing evidence form of biometrics for verification. However, the verification of offline handwritten signatures is challenging task because of the variations in handwritten signatures. To address this difficulty, this paper proposes a new approach to represent the shape. In this newly proposed approach, the signature pixels are represented by: (1) Gaussian Weighting Based Tangent Angle, to represent the curve angle at the reference pixel; (2) a new shape descriptor, i.e. cylindrical shape context is proposed for a detailed and accurate description of the curve at corresponding pixel. Experimental results show that desired pixel matching results are obtained by using cylindrical shape context which automatically increases the accuracy of verification of offline handwritten signatures. The shape dissimilarity measures are computed and given to the Support Vector Machine with Radial Basis Function (RBF) kernel for classification of signature. The results obtained using GPDS synthetic signature database, UTSig persian offline signature database, and MCYT-75 offline signature database shows the effectiveness of proposed cylindrical shape context.

Keywords

Offline signature verification Cylindrical shape context Shape descriptor Biometrics Shape matching Shape dissimilarity measures 

References

  1. 1.
    Steinherz, T., Doermann, D., Member, S., Rivlin, E., & Intrator, N. (2009). Offline loop investigation for handwriting analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 193–209.CrossRefGoogle Scholar
  2. 2.
    Nguyen, V., Blumenstein, M., & Leedham, G. (2009). Global features for the off-line signature verification problem. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR (pp. 1300–1304).Google Scholar
  3. 3.
    Pal, U., Blumenstein, M., & Pal, S. (2013). Off-line verification technique for Hindi signatures. IET Biometrics, 2(4), 182–190.CrossRefGoogle Scholar
  4. 4.
    Diaz, M., Fischer, A., Ferrer, M. A., & Plamondon, R. (2016). Dynamic signature verification system based on one real signature. IEEE Transactions on Cybernetics, 48(1), 1–12.Google Scholar
  5. 5.
    Al-Hmouz, R., Pedrycz, W., Daqrouq, K., Morfeq, A., & Al-Hmouz, A. (2017). Quantifying dynamic time warping distance using probabilistic model in verification of dynamic signatures. Soft Computing.  https://doi.org/10.1007/s00500-017-2782-5.CrossRefzbMATHGoogle Scholar
  6. 6.
    Azmi, A. N., Nasien, D., & Omar, F. S. (2017). Biometric signature verification system based on freeman chain code and k-nearest neighbor. Multimedia Tools and Applications, 76(14), 15341–15355.CrossRefGoogle Scholar
  7. 7.
    Zois, E. N., Alewijnse, L., & Economou, G. (2016). Offline signature verification and quality characterization using poset-oriented grid features. Pattern Recognition, 54, 162–177.CrossRefGoogle Scholar
  8. 8.
    Sulong, G., Ebrahim, A. Y., & Jehanzeb, M. (2014). Offline handwritten signature identification using adaptive window positioning techniques. Signal & Image Processing: An International Journal (SIPIJ), 5(3), 13–24.Google Scholar
  9. 9.
    Fang, B., Leung, C. H., Tang, Y. Y., Tse, K. W., Kwok, P. C. K., & Wong, Y. K. (2003). Off-line signature verification by the tracking of feature and stroke positions. Pattern Recognition, 36(1), 91–101.CrossRefGoogle Scholar
  10. 10.
    Ferrer, M. A., Alonso, J. B., & Travieso, C. M. (2005). Offline geometric parameters for automatic signature verification using fixed-point arithmetic. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 993–997.CrossRefGoogle Scholar
  11. 11.
    Batista, L., Granger, E., & Sabourin, R. (2012). Dynamic selection of generative-discriminative ensembles for off-line signature verification. Pattern Recognition, 45(4), 1326–1340.CrossRefGoogle Scholar
  12. 12.
    Soleimani, A., Fouladi, K., & Araabi, B. N. (2016). Persian offline signature verification based on curvature and gradient histograms. In 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), (pp. 147–152).Google Scholar
  13. 13.
    Kumar, R., Sharma, J. D., & Chanda, B. (2012). Writer-independent off-line signature verification using surroundedness feature. Pattern Recognition Letters, 33(3), 301–308.CrossRefGoogle Scholar
  14. 14.
    Ferrer, M. A., Vargas, J. F., Morales, A., & Ordóñez, A. (2012). Robustness of offline signature verification based on gray level features. IEEE Transactions on Information Forensics and Security, 7(3), 966–977.CrossRefGoogle Scholar
  15. 15.
    Serdouk, Y., Nemmour, H., & Chibani, Y. (2016). New off-line handwritten signature verification method based on artificial immune recognition system. Expert Systems with Applications, 51, 186–194.CrossRefGoogle Scholar
  16. 16.
    Vargas, J. F., Ferrer, M. A., Travieso, C. M., & Alonso, J. B. (2011). Off-line signature verification based on grey level information using texture features. Pattern Recognition, 44(2), 375–385.CrossRefGoogle Scholar
  17. 17.
    Alaei, A., Pal, S., Pal, U., & Blumenstein, M. (2017). An efficient signature verification method based on an interval symbolic representation and a fuzzy similarity measure. IEEE Transactions on Information Forensics and Security, 12(10), 2360–2372.CrossRefGoogle Scholar
  18. 18.
    Neamah, K., Mohamad, D., Saba, T., & Rehman, A. (2014). Discriminative features mining for offline handwritten signature verification. 3D Research, 5(1), 1–6.Google Scholar
  19. 19.
    Kumar, M. M., & Puhan, N. B. (2014). Off-line signature verification: upper and lower envelope shape analysis using chord moments. IET Biometrics, 3(4), 347–354.CrossRefGoogle Scholar
  20. 20.
    Maergner, P., Riesen, K., Ingold, R., & Fischer, A. (2017). A structural approach to offline signature verification using graph edit distance. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) (pp. 1216–1222).Google Scholar
  21. 21.
    Fierrez-Aguilar, J., Alonso-Hermira, N., Moreno-Marquez, G., & Ortega-Garcia, J. (2004). An off-line signature verification system based on fusion of local and global information. In International Workshop on Biometric Authentication (pp. 295–306).CrossRefGoogle Scholar
  22. 22.
    Sharif, M., Khan, M. A., Faisal, M., Yasmin, M., & Fernandes, S. L. (2018). A framework for offline signature verification system: Best features selection approach. Pattern Recognition Letters.  https://doi.org/10.1016/j.patrec.2018.01.021.CrossRefGoogle Scholar
  23. 23.
    Soleimani, A., Araabi, B. N., & Fouladi, K. (2016). Deep multitask metric learning for offline signature verification. Pattern Recognition Letters, 80, 84–90.CrossRefGoogle Scholar
  24. 24.
    Guerbai, Y., Chibani, Y., & Hadjadji, B. (2015). The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recognition, 48(1), 103–113.CrossRefGoogle Scholar
  25. 25.
    Ooi, S. Y., Teoh, A. B. J., Pang, Y. H., & Hiew, B. Y. (2016). Image-based handwritten signature verification using hybrid methods of discrete radon transform, principal component analysis and probabilistic neural network. Applied Soft Computing, 40, 274–282.CrossRefGoogle Scholar
  26. 26.
    Hamadene, A., & Chibani, Y. (2016). One-class writer-independent offline signature verification using feature dissimilarity thresholding. IEEE Transactions on Information Forensics and Security, 11(6), 1226–1238.CrossRefGoogle Scholar
  27. 27.
    Ruiz-del-Solar, J., Devia, C., Loncomilla, P., & Concha, F. (2008). Offline signature verification using local interest points and descriptors. Iberoamerican Congress on Pattern Recognition, 22–29.Google Scholar
  28. 28.
    Hadjadji, B., Chibani, Y., & Nemmour, H. (2017). An efficient open system for offline handwritten signature identification based on curvelet transform and one-class principal component analysis. Neurocomputing, 265, 66–77.CrossRefGoogle Scholar
  29. 29.
    Bharathi, R. K., & Shekar, B. H. (2013). Off-line signature verification based on chain code histogram and Support Vector Machine. In International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 2063–2068).Google Scholar
  30. 30.
    Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine, 24(24), 509–522.CrossRefGoogle Scholar
  31. 31.
    Ling, H., Member, S., & Jacobs, D. W. (2007). Shape Classification Using the Inner-Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2), 286–299.CrossRefGoogle Scholar
  32. 32.
    Mori, G., Belongie, S., & Malik, J. (2005). Efficient shape matching using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), 1832–1837.CrossRefGoogle Scholar
  33. 33.
    Narwade, P. N., Bonde, S. V, & Doye, D. D. (2015). Offline signature verification using shape dissimilarities. In International Conference on Communication, Information & Computing Technology (ICCICT) (pp. 1–6).Google Scholar
  34. 34.
    Narwade, P. N., Sawant, R. R., & Bonde, S. V. (2018). Offline signature verification using shape correspondence. International Journal of Biometrics, 10(3), 272–289.CrossRefGoogle Scholar
  35. 35.
    Attalla, E., & Siy, P. (2005). Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching. Pattern Recognition, 38(12), 2229–2241.CrossRefGoogle Scholar
  36. 36.
    Berretti, S., Del Bimbo, A., & Pala, P. (2000). Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Transactions on Multimedia, 2(4), 225–239.CrossRefGoogle Scholar
  37. 37.
    Dudek, G., & Tsotsos, J. K. (1997). Shape representation and recognition from multiscale curvature. Computer Vision and Image Understanding, 68(2), 170–189.CrossRefGoogle Scholar
  38. 38.
    Alajlan, N., Kamel, M. S., & Freeman, G. (2008). Geometry-based image retrieval in binary image databases. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), 1003–1013.CrossRefGoogle Scholar
  39. 39.
    Van Nguyen, H., & Porikli, F. (2013). Support vector shape: A classifier-based shape representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4), 970–982.CrossRefGoogle Scholar
  40. 40.
    Senthilnayaki, M., Veni, S., & Kutty, K. A. N. (2006). Hexagonal pixel grid modeling for edge detection and design of Cellular architecture for binary image skeletonization. In 2006 Annual India Conference, INDICON (pp. 1–6).Google Scholar
  41. 41.
    Deutsch, E. S. (1972). Thinning algorithms on rectangular, hexagonal, and triangular arrays. Communications of the ACM, 15(9), 827–837.CrossRefGoogle Scholar
  42. 42.
    Kuhn, H. W. (2005). The Hungarian method for the assignment problem. Naval Research Logistics (NRL), 52(1), 7–21.MathSciNetCrossRefGoogle Scholar
  43. 43.
    Ferrer, M. A., Diaz-Cabrera, M., & Morales, A. (2015). Static signature synthesis: A neuromotor inspired approach for biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 667–680.CrossRefGoogle Scholar
  44. 44.
    Ferrer, M. A., Diaz-Cabrera, M., & Morales, A. (2013). Synthetic off-line signature image generation. In Proceedings2013 International Conference on Biometrics, ICB 2013 (pp. 1–7).Google Scholar
  45. 45.
    Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., et al. (2003). MCYT baseline corpus: a bimodal biometric database. IEE Proceedings-Vision, Image and Signal Processing, 150(6), 395–401.CrossRefGoogle Scholar
  46. 46.
    Soleimani, A., Fouladi, K., & Araabi, B. N. (2016). UTSig: A Persian offline signature dataset. IET Biometrics, 6(1), 1–8.CrossRefGoogle Scholar
  47. 47.
    Scholkopf, B., & Smola, A. J. (2001). Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT press.Google Scholar

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Pradeep N. Narwade
    • 1
  • Rajendra R. Sawant
    • 2
  • Sanjiv V. Bonde
    • 1
  1. 1.Department of Electronics and TelecommunicationS.G.G.S.I.E & T.NandedIndia
  2. 2.Research and Development, Inventronics Pvt. LtdMumbaiIndia

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