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Open writer identification from offline handwritten signatures by jointing the one-class symbolic data analysis classifier and feature-dissimilarities


Usually, a large number of reference signatures are required for building the writing style model from offline handwritten signatures (OHSs). Moreover, the existing writer identification systems from OHSs are generally closed systems that require a retraining process when a new writer is added. This paper proposes an open writer identification system from OHSs, based on a new scheme of the one-class symbolic data analysis (OC-SDA) classifier, using few reference signatures. For generating more data, intra-class feature-dissimilarities, generated from curvelet transform, are introduced for building the symbolic representation model (SRM) associated with each writer. Feature-dissimilarities allow capturing more efficiently the intra-personnel variability produced naturally by a writer and, thus, increase the inter-personnel variability. Instead of using the mean and the standard deviation for building the OC-SDA model, intra-class feature-dissimilarities generated for each writer are modeled through a new weighted membership function, inspired from the real probability distribution of training intra-class feature-dissimilarities. The comparative analysis against the state-of-the-art works shows that the proposed OC-SDA classifier outperforms the existing classifiers on three public signature datasets GPDS-300, CEDAR-55 and MCYT-75, using only five reference signatures, achieving 98.31%, 98.06% and 99.89%, respectively, even when a combination of multiple classifiers is performed or even using learned features. Moreover, the evaluation of the proposed writer identification system in front of skilled forgeries shows its ability to detect also possible forged signatures in addition to the genuine ones.

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  1. Alaei, A., Roy, P.P.: A new method for writer identification based on histogram symbolic representation. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 216–221. IEEE (2014).

  2. Alaei, A., Pal, S., Pal, U., Blumenstein, M.: An efficient signature verification method based on an interval symbolic representation and a fuzzy similarity measure. IEEE Trans. Inf. Forensics Secur. 12(10), 2360–2372 (2017).

    Article  Google Scholar 

  3. Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recognit. 43(1), 387–396 (2010).

    Article  MATH  Google Scholar 

  4. Boyer, K.W., Govindaraju, V., Ratha, N.K.: Introduction to the special issue on recent advances in biometric systems [guest editorial]. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 37(5), 1091–1095 (2007)

    Article  Google Scholar 

  5. Çalik, N., Kurban, O.C., Yilmaz, A.R., Yildirim, T., Ata, L.D.: Large-scale offline signature recognition via deep neural networks and feature embedding. Neurocomputing 359, 1–14 (2019).

    Article  Google Scholar 

  6. Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006).

    MathSciNet  Article  MATH  Google Scholar 

  7. Candes, E., Donoho, D.: A Surprisingly Effective Non adaptive Representation for Objects with Edges, Curves and Surfaces. Vanderbilt University Press, Nashville (2000)

    Google Scholar 

  8. De Carvalho, D.A.F., de Souza, R.M., Chavent, M., Lechevallier, Y.: Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognit. Lett. 27(3), 167–179 (2006).

    Article  Google Scholar 

  9. Eskander, G.S., Sabourin, R., Granger, E.: Dissimilarity representation for handwritten signature verification (2013)

  10. Feng, K., Jiang, Z., He, W., Ma, B.: A recognition and novelty detection approach based on curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis. Expert Syst. Appl. 38(10), 12721–12729 (2011).

    Article  Google Scholar 

  11. Foroozandeh, A., Hemmat, A.A., Rabbani, H.: Offline handwritten signature verification and recognition based on deep transfer learning. In: 2020 International Conference on Machine Vision and Image Processing (MVIP), pp. 1–7. IEEE (2020)

  12. Frias-Martinez, E., Sanchez, A., Velez, J.: Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition. Eng. Appl. Artif. Intell. 19(6), 693–704 (2006).

    Article  Google Scholar 

  13. Ghosh, R.: A recurrent neural network based deep learning model for offline signature verification and recognition system. Expert Syst. Appl. 168, 114249 (2021)

    Article  Google Scholar 

  14. Gowda, K.C., Diday, E.: Symbolic clustering using a new dissimilarity measure. Pattern Recognit. 24(6), 567–578 (1991).

    Article  Google Scholar 

  15. 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. 48(1), 103–113 (2015).

    Article  Google Scholar 

  16. Guru, D., Kiranagi, B.B., Nagabhushan, P.: Multivalued type proximity measure and concept of mutual similarity value useful for clustering symbolic patterns. Pattern Recognit. Lett. 25(10), 1203–1213 (2004).

    Article  Google Scholar 

  17. Hadjadji, B., Chibani, Y., Nemmour, H.: An efficient open system for offline handwritten signature identification based on curvelet transform and one-class principal component analysis. Neurocomputing 265, 66–77 (2017).

    Article  Google Scholar 

  18. Hamadene, A., Chibani, Y.: One-class writer-independent offline signature verification using feature dissimilarity thresholding. IEEE Trans. Inf. Forensics Secur. 11(6), 1226–1238 (2016).

    Article  Google Scholar 

  19. Han, K., Sethi, I.K.: Handwritten signature retrieval and identification. Pattern Recognit. Lett. 17(1), 83–90 (1996).

    Article  Google Scholar 

  20. Hiremath, P., Prabhakar, C.: Symbolic factorial discriminant analysis for face recognition under variable lighting (2006).

  21. Ismail, M., Gad, S.: Off-line Arabic signature recognition and verification. Pattern Recognit. 33(10), 1727–1740 (2000).

    Article  Google Scholar 

  22. Jain, A., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004).

    Article  Google Scholar 

  23. Kalera, M.K., Srihari, S., Xu, A.: Offline signature verification and identification using distance statistics. Int. J. Pattern Recognit. Artif. Intell. 18(07), 1339–1360 (2004).

    Article  Google Scholar 

  24. Kalera, M.K., Srihari, S., Xu, A.: Offline signature verification and identification using distance statistics. Int. J. Pattern Recognit. Artif. Intell. 18(07), 1339–1360 (2004).

    Article  Google Scholar 

  25. Kumar, R., Sharma, J., Chanda, B.: Writer-independent off-line signature verification using surroundedness feature. Pattern Recognit. Lett. 33(3), 301–308 (2012).

    Article  Google Scholar 

  26. Kumari, K., Rana, S.: Offline signature recognition using deep features. In: Joshi, A. (ed.) Machine Learning for Predictive Analysis, pp. 405–421. Springer, Berlin (2021)

    Chapter  Google Scholar 

  27. Li, Y., Yang, Q., Jiao, R.: Image compression scheme based on curvelet transform and support vector machine. Expert Syst. Appl. 37(4), 3063–3069 (2010).

    Article  Google Scholar 

  28. Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., et al.: MCYT baseline corpus: a bimodal biometric database. IEE Proc. Vis. Image Signal Process. 150(6), 395–401 (2003).

    Article  Google Scholar 

  29. Pal, S., Alaei, A., Pal, U., Blumenstein, M.: Interval-valued symbolic representation based method for off-line signature verification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2015).

  30. Pavlidis, I., Papanikolopoulos, N.P., Mavuduru, R.: Signature identification through the use of deformable structures. Signal Process. 71(2), 187–201 (1998).

    Article  MATH  Google Scholar 

  31. Pękalska, E., Duin, R.P.: Dissimilarity representations allow for building good classifiers. Pattern Recognit. Lett. 23(8), 943–956 (2002).

    Article  MATH  Google Scholar 

  32. Prakash, H., Guru, D.: Offline signature verification: an approach based on score level fusion. Int. J. Comput. Appl. 10(1), 52–58 (2010).

    Article  Google Scholar 

  33. Rajaei, A., Dallalzadeh, E., Rangarajan, L.: Symbolic representation and classification of medical X-ray images. Signal Image Video Process. 9(3), 715–725 (2015).

    Article  Google Scholar 

  34. Riba, J.R., Carnicer, A., Vallmitjana, S., Juvells, I.: Methods for invariant signature classification. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, vol. 2, pp. 953–956. IEEE (2000).

  35. Santos, C., Justino, E.J., Bortolozzi, F., Sabourin, R.: An off-line signature verification method based on the questioned document expert’s approach and a neural network classifier. In: Ninth International Workshop on Frontiers in Handwriting Recognition, pp. 498–502. IEEE (2004).

  36. Sigari, M.H., Pourshahabi, M.R., Pourreza, H.R.: Offline handwritten signature identification and verification using multi-resolution Gabor wavelet. Int. J. Biometr. Bioinform. (IJBB) 5(4), 234–248 (2011)

    Google Scholar 

  37. Srihari, S.N., Xu, A., Kalera, M.K.: Learning strategies and classification methods for off-line signature verification. In: Ninth International Workshop on Frontiers in Handwriting Recognition, pp. 161–166. IEEE (2004).

  38. Vargas, F., Ferrer, M., Travieso, C., Alonso, J.: Off-line handwritten signature GPDS-960 corpus. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, pp. 764–768. IEEE (2007).

  39. Vikram, T., Gowda, K.C., Urs, S.R.: Symbolic representation of kannada characters for recognition. In: 2008 IEEE International Conference on Networking, Sensing and Control, pp. 823–826. IEEE (2008).

  40. Villager, C., Dittmann, J.: Biometrics for User Authentication, pp. 35–44. Springer US, Boston (2006).

    Book  Google Scholar 

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This work was supported by the Direction Générale de la Recherche Scientifique et du Développement Technologique (DGRSDT) Grant, attached to the Ministère de l’Enseignement Supérieur et de la Recherche Scientifique, Algeria.

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Correspondence to Mohamed Anis Djoudjai.

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Djoudjai, M.A., Chibani, Y. Open writer identification from offline handwritten signatures by jointing the one-class symbolic data analysis classifier and feature-dissimilarities. IJDAR (2022).

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  • Signature identification
  • Open system
  • One-class symbolic data analysis classifier
  • Feature-dissimilarities
  • Curvelet transform