Interval-Valued Writer-Dependent Global Features for Off-line Signature Verification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10682)

Abstract

This work focuses on the proposal of a method for Off-line signature verification based on selecting writer-dependent global Features. 150 Global features of different categories namely geometrical, texture based, statistical and grid features for offline signatures are computed. Writer dependent features are selected through an application of a filter based feature selection method. Further, to preserve the intra-writer variations effectively, the selected features are represented by interval-valued data through aggregation of samples of each writer. Here in this work, we recommend creating two interval valued feature vectors for each writer. Decision on the test signature is accomplished by means of a symbolic classifier. In the first stage, we conducted experiments with writer dependent features by keeping a common dimension for all writers. Further, we conducted experiments with varying writer dependent feature dimension and threshold as done by a human expert. To demonstrate the effectiveness of the proposed approach extensive experimentation has been conducted on both CEDAR and MCYT offline signature datasets. The Error-rate obtained with the proposed model is low in comparision with many of contemporary models.

Keywords

Off-line signature Global features Interval valued data Writer dependent features symbolic classifier 

References

  1. 1.
    Plamondon, R., Lorette, G.: Automatic signature verification and writer identification - the state of the art. Pattern Recogn. 2, 107–131 (1989)CrossRefGoogle Scholar
  2. 2.
    Jain, A.K., Griess, F.D., Connell, S.D.: On-line signature verification. Pattern Recogn. 35, 2963–2972 (2002)CrossRefMATHGoogle Scholar
  3. 3.
    Qi, Y., Hunt, B.R.: Signature verification using global and grid features. Pattern Recogn. 27(12), 1621–1629 (1994)CrossRefGoogle Scholar
  4. 4.
    Huang, K., Yan, H.: Off-line signature verification based on geometric feature extraction and neural network classification. Pattern Recogn. 30, 9–17 (1997)CrossRefGoogle Scholar
  5. 5.
    Karouni, A., Daya, B., Bahlak, S.: Offline signature recognition using neural networks approach. Procedia Comput. Sci. 3, 155–161 (2011)CrossRefGoogle Scholar
  6. 6.
    Hatkar, P.V., Salokhe, B.T., Malgave, A.A.: Off-line handwritten signature verification using neural network. Int. J. Innov. Eng. Res. Technol. 2(1), 1–5 (2015)Google Scholar
  7. 7.
    Vargas, J.F., Ferrer, M.A., Travieso, C.M., Alonso, J.B.: Off-line signature verification based on grey level information using texture features. Pattern Recogn. 44, 375–385 (2011)Google Scholar
  8. 8.
    Nguyen, V., Kawazoey, Y., Wakabayashiy, T., Pal, U., Blumenstein, M.: Performance analysis of the gradient feature and the modified direction feature for off-line signature verification. In: Proceeding of IEEE 12th International Conference on Frontiers in Handwriting Recognition, pp. 303–307 (2010)Google Scholar
  9. 9.
    Mhatre, P.M., Maniroja, M.: Offline signature verification based on statistical features. Published in Proceedings of International Conference & Workshop on Emerging Trends in Technology, pp. 59–62 (2011)Google Scholar
  10. 10.
    Gilperez, A., Fernandez, F.A., Pecharroman, S., Fierrez, J., Garcia, J.O.: Off-line signature verification using contour features. In: ICFHR, pp. 1–6 (2013)Google Scholar
  11. 11.
    Prakash, H.N., Guru, D.S.: Relative orientations of geometric centroids for off-line signature verification. In: ICAPR, pp. 201–204 (2009)Google Scholar
  12. 12.
    Lv, H., Wang, W., Wang, C., Zhuo, Q.: Off-line Chinese signature verification based on support vector machines. Pattern Recongn. Lett. 26, 2390–2399 (2005)CrossRefGoogle Scholar
  13. 13.
    Parodi, M., Gomez, J.C., Belaid, A.: A circular grid-based rotation invariant feature extraction approach for off-line signature verification. In: ICDAR, pp. 1289–1293 (2011)Google Scholar
  14. 14.
    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 Recogn. 48, 103–113 (2015)CrossRefGoogle Scholar
  15. 15.
    Coetzer, J., Herbst, B.M., duPreez, J.A.: Offline signature verification using the discrete radon transform and a hidden Markov model. EURASIP J. Appl. Sig. Process. 4, 559–571 (2004)CrossRefGoogle Scholar
  16. 16.
    Daramola, D.S.A., Ibiyemi, P.T.S.: Offline signature recognition using hidden markov model (HMM). Int. J. Comput. Appl. 10, 17–22 (2010)Google Scholar
  17. 17.
    Eskander, G.S., Sabourin, R., Granger, E.: Hybrid writer-independent –writer –dependent offline signature verification system. IET Biometrics 2(4), 169–181 (2013)CrossRefGoogle Scholar
  18. 18.
    Srihari, S.N., Xu, A., Kalera, M.K.: Learning strategies and classification methods for off-line signature verification. In: IWFHR, pp. 1–6 (2004)Google Scholar
  19. 19.
    Guru, D.S., Manjunatha, K.S., Manjunath, S.: User dependent features in online signature verification. In: Swamy, P., Guru, D. (eds.) Multimedia Processing, Communication and Computing Applications. LNEE, vol. 213, pp. 229–239. Springer, New Delhi (2013).  https://doi.org/10.1007/978-81-322-1143-3_19 CrossRefGoogle Scholar
  20. 20.
    Manjunatha, K.S., Manjunath, S., Guru, D.S., Somashekara, M.T.: Online signature verification based on writer dependent features and classifiers. Pattern Recogn. Lett. 80, 129–136 (2016)CrossRefGoogle Scholar
  21. 21.
    Alaei, A., Pal, S., Pal, U.: An efficient signature verification method based on interval symbolic representation and Fuzzy similarity measure. IEEE Trans. Inf. Forensics Secur. 12(10), 2360–2372 (2017)CrossRefGoogle Scholar
  22. 22.
    Ramachandra, A.C., Rao, J.S., Raja, K.B., Venugopla, K.R., Patnaik, L.M.: Robust offline signature verification based on global features. Published in IEEE International Advance Computing Conference, pp. 1173–1178 (2009)Google Scholar
  23. 23.
    Kruthi, C., Shet, D.C.: Offline signature verification using support vector machine. In: IEEE Transactions (2014).  https://doi.org/10.1109/ICSIP.2014.5
  24. 24.
    Cai, D., Zhang, C., He, X.: Unsupervised feature selection for multi-cluster data. In: International Conference on Knowledge Discovery and Data Mining, pp. 333–342 (2010)Google Scholar
  25. 25.
    Kalera, M.K., Srihari, S., Xu, A.: Offline signature verification and identification using distance statistics. Int. J. Pattern Recogn. Artif. Intell. (IJPRAI) 18(7), 1339–1360 (2004)CrossRefGoogle Scholar
  26. 26.
    Garcia, O.J., Aguiliar, J.F., Simon, D.: MCYT baseline corpus: a bimodal database. In: IEE Proceedings Vision, Image and Signal Processing, pp. 395–401 (2003)Google Scholar
  27. 27.
    Kumar, R., Sharma, J.D., Chanda, B.: Writer-independent off-line signature verification using surroundedness feature. Pattern Recogn. Lett. 33, 301–308 (2012)CrossRefGoogle Scholar
  28. 28.
    Hafemann, L.G., Sabourin, R., Oliveira, L.S.: Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recogn. 70, 163–176 (2017)CrossRefGoogle Scholar
  29. 29.
    Chen, S., Srihari, S.: A new off-line signature verification method based on graph. In: Proceedings of 18th International Conference on Pattern Recognition, pp. 869–872 (2006)Google Scholar
  30. 30.
    Bharathi, R., Shekar, B.: Off-line signature verification based on chain code histogram and support vector machine. Published in International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2063–2013, 2068.  https://doi.org/10.1109/ICACCI.2013.6637499
  31. 31.
    Soleimani, A., Araabi, B.N., Fouladi, K.: Deep multitask metric learning for offline signature verification. Pattern Recogn. Lett. 80, 84–90 (2016)CrossRefGoogle Scholar
  32. 32.
    Ooi, S.Y., Teoh, A.B.J., Pang, Y.H., Hiew, B.Y.: Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network. Appl. Soft Comput. 40, 274–282 (2016)CrossRefGoogle Scholar
  33. 33.
    Wen, J., Fang, B., Tang, Y.Y., Zhang, T.: Model-based signature verification with rotation invariant features. Pattern Recogn. 42, 1458–1466 (2009)CrossRefMATHGoogle Scholar
  34. 34.
    Ferrer, M.A., Vargas, J.F., Morales, A., Ordóñez, A.: Robustness of offline signature verification based on gray level features. IEEE Trans. Inf. Forensic Secur. 7(3), 966–977 (2012)CrossRefGoogle Scholar
  35. 35.
    Alonso-Fernandeza, F., Fairhurstb, M.C., Fierreza, J., Ortega-Garciaa, J.: Automatic measures for predicting performance in off-line signature. In: ICIP, vol. I, pp. 369–372 (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Maharani’s Science College for WomenMysuruIndia
  2. 2.Department of Studies in Computer ScienceUniversity of Mysore, ManasagangothriMysuruIndia

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