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Usage of autoencoders and Siamese networks for online handwritten signature verification

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Abstract

In this paper, we propose a novel writer-independent global feature extraction framework for the task of automatic signature verification which aims to make robust systems for automatically distinguishing negative and positive samples. Our method consists of an autoencoder for modeling the sample space into a fixed-length latent space and a siamese network for classifying the fixed-length samples obtained from the autoencoder based on the reference samples of a subject as being genuine or forged. During our experiments, usage of attention mechanism and applying downsampling significantly improved the accuracy of the proposed framework. We evaluated our proposed framework using SigWiComp2013 Japanese and GPDSsyntheticOnLineOffLineSignature datasets. On the SigWiComp2013 Japanese dataset, we achieved 8.65% equal error rate (EER) that means 1.2% relative improvement compared to the best-reported result. Furthermore, on the GPDSsyntheticOnLineOffLineSignature dataset, we achieved average EERs of 0.13%, 0.12%, 0.21% and 0.25%, respectively, for 150, 300, 1000 and 2000 test subjects which indicate improvement in relative EER on the best-reported result by 95.67%, 95.26%, 92.9% and 91.52%, respectively. Apart from the accuracy gain, because of the nature of our proposed framework which is based on neural networks and consequently is as simple as some consecutive matrix multiplications, it has less computational cost than conventional methods such as Dynamic Time Warping and could be used concurrently on devices such as Graphics Processing Unit and Tensor Processing Unit.

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References

  1. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

  2. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5:157–166

    Article  Google Scholar 

  3. Bengio Y et al (2009) Learning deep architectures for AI. Found Trends Mach Learn 2:1–127

    Article  Google Scholar 

  4. Bromley J, Guyon I, LeCun Y, Säckinger E, Shah R (1994) Signature verification using a “Siamese” time delay neural network. In: Advances in neural information processing systems, pp 737–744

  5. Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder–decoder approaches. arXiv preprint arXiv:1409.1259

  6. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

  7. Chollet F et al (2015) Keras: Deep learning library for theano and tensorflow. vol 7, p 8. https://keras.io/k

  8. Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: IEEE computer society conference on computer vision and pattern recognition, CVPR 2005, IEEE, vol 1, pp 539–546

  9. Dey S, Dutta A, Toledo JI, Ghosh SK, Lladós J, Pal U (2017) Signet: convolutional Siamese network for writer independent offline signature verification. arXiv preprint arXiv:1707.02131

  10. Dolfing J, Aarts EH, Van Oosterhout J (1998) On-line signature verification with hidden markov models. In: Proceedings of fourteenth international conference on pattern recognition, IEEE, vol 2, pp 1309–1312

  11. Du W, Fang M, Shen M (2017) Siamese convolutional neural networks for authorship verification. CourseWork report, Stanford University

  12. Ferrer MA, Diaz M, Carmona-Duarte C, Morales A (2017) A behavioral handwriting model for static and dynamic signature synthesis. IEEE Trans Pattern Anal Mach Intell 39:1041–1053

    Article  Google Scholar 

  13. Fierrez J, Ortega-Garcia J, Ramos D, Gonzalez-Rodriguez J (2007) Hmm-based on-line signature verification: feature extraction and signature modeling. Pattern Recognit Lett 28:2325–2334

    Article  Google Scholar 

  14. Fierrez-Aguilar J, Nanni L, Lopez-Peñalba J, Ortega-Garcia J, Maltoni D (2005) An on-line signature verification system based on fusion of local and global information. In: AVBPA, Springer, vol 5, pp 523–532

  15. Fischer A, Diaz M, Plamondon R, Ferrer MA (2015) Robust score normalization for dtw-based on-line signature verification. In: 2015 13th international conference on document analysis and recognition (ICDAR), IEEE, pp 241–245

  16. Gal Y, Ghahramani Z (2016) A theoretically grounded application of dropout in recurrent neural networks. In: Advances in neural information processing systems, pp 1019–1027

  17. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

  18. Gregor K, Danihelka I, Graves A, Rezende DJ, Wierstra D (2015) Draw: a recurrent neural network for image generation. arXiv preprint arXiv:1502.04623

  19. Hafemann LG, Sabourin R, Oliveira LS (2015) Offline handwritten signature verification-literature review. arXiv preprint arXiv:1507.07909

  20. Hafemann LG, Sabourin R, Oliveira LS (2016) Analyzing features learned for offline signature verification using deep cnns. In: 2016 23rd international conference on pattern recognition (ICPR), IEEE, pp 2989–2994

  21. Hochreiter S, Bengio Y, Frasconi P, Schmidhuber J. (2001) Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: Kremer SC, Kolen JF (eds) A field guide to dynamical recurrent neural networks. IEEE Press, New York. https://www.bibsonomy.org/bibtex/279df6721c014a00bfac62abd7d5a9968/schaul

  22. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  23. Humm A, Hennebert J, Ingold R (2006) Gaussian mixture models for chasm signature verification. In: International workshop on machine learning for multimodal interaction, Springer, pp 102–113

  24. Impedovo D, Pirlo G (2008) Automatic signature verification: the state of the art. IEEE Trans Syst Man Cybern Part C Appl Rev 38:609–635

    Article  Google Scholar 

  25. Jain AK, Griess FD, Connell SD (2002) On-line signature verification. Pattern Recognit 35:2963–2972

    Article  Google Scholar 

  26. Kholmatov A, Yanikoglu B (2005) Identity authentication using improved online signature verification method. Pattern Recognit Lett 26:2400–2408

    Article  Google Scholar 

  27. Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  28. Koch G, Zemel R, Salakhutdinov R (2015) Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, vol 2

  29. Lee LL, Berger T, Aviczer E (1996) Reliable online human signature verification systems. IEEE Trans Pattern Anal Mach Intell 18:643–647

    Article  Google Scholar 

  30. Lei H, Govindaraju V (2005) A comparative study on the consistency of features in on-line signature verification. Pattern Recognit Lett 26:2483–2489

    Article  Google Scholar 

  31. Lejtman DZ, George SE (2001) On-line handwritten signature verification using wavelets and back-propagation neural networks. In: Proceedings of sixth international conference on document analysis and recognition, IEEE, pp 992–996

  32. Liu Y, Yang Z, Yang L (2015) Online signature verification based on dct and sparse representation. IEEE Trans Cybern 45:2498–2511

    Article  Google Scholar 

  33. Malik MI, Liwicki M, Alewijnse L, Ohyama W, Blumenstein M, Found B (2013) Icdar 2013 competitions on signature verification and writer identification for on-and offline skilled forgeries (sigwicomp 2013). In: 2013 12th international conference on document analysis and recognition (ICDAR), IEEE, pp 1477–1483

  34. Martens R, Claesen L (1997) Dynamic programming optimisation for on-line signature verification. In: Proceedings of the fourth international conference on document analysis and recognition, IEEE, vol 2, pp 653–656

  35. Miguel-Hurtado O, Mengibar-Pozo L, Lorenz MG, Liu-Jimenez J (2007) On-line signature verification by dynamic time warping and gaussian mixture models. In: 2007 41st annual IEEE international Carnahan conference on security technology, IEEE, pp 23–29

  36. Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807–814

  37. Nanni L (2006) An advanced multi-matcher method for on-line signature verification featuring global features and tokenised random numbers. Neurocomputing 69:2402–2406

    Article  Google Scholar 

  38. Nanni L (2006) Experimental comparison of one-class classifiers for online signature verification. Neurocomputing 69:869–873

    Article  Google Scholar 

  39. Nanni L, Lumini A (2005) Ensemble of parzen window classifiers for on-line signature verification. Neurocomputing 68:217–224

    Article  Google Scholar 

  40. Prechelt L (1998) Automatic early stopping using cross validation: quantifying the criteria. Neural Netw 11:761–767

    Article  Google Scholar 

  41. Richiardi J, Drygajlo A (2003) Gaussian mixture models for on-line signature verification. In: Proceedings of the 2003 ACM SIGMM workshop on biometrics methods and applications, ACM, pp 115–122

  42. Richiardi J, Ketabdar H, Drygajlo A (2005) Local and global feature selection for on-line signature verification. In: Proceedings of eighth international conference on document analysis and recognition, IEEE, pp 625–629

  43. Rúa EA, Castro JLA (2012) Online signature verification based on generative models. IEEE Trans Syst Man Cybern Part B Cybern 42:1231–1242

    Google Scholar 

  44. Sae-Bae N, Memon N (2014) Online signature verification on mobile devices. IEEE Trans Inf Forensics Secur 9:933–947

    Article  Google Scholar 

  45. Sati Y (1982) On-line signature verification based on shape, motion and handwriting pressure. In: Proceedings of 6th international conference on pattern recognition, vol 2, pp 823–826

  46. Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45:2673–2681

    Article  Google Scholar 

  47. Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    MathSciNet  MATH  Google Scholar 

  48. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112

  49. Team TD (2016) Theano: a python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688

  50. Van BL, Garcia-Salicetti S, Dorizzi B (2007) On using the viterbi path along with HMM likelihood information for online signature verification. IEEE Trans Syst Man Cybern Part B Cybern 37:1237–1247

    Article  Google Scholar 

  51. Vivaracho-Pascual C, Faundez-Zanuy M, Pascual JM (2009) An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances. Pattern Recognit 42:183–193

    Article  Google Scholar 

  52. Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: neural image caption generation with visual attention. In: International conference on machine learning, pp 2048–2057

  53. Yang L, Widjaja B, Prasad R (1995) Application of hidden markov models for signature verification. Pattern Recognit 28:161–170

    Article  Google Scholar 

  54. Yang Z, Yang D, Dyer C, He X, Smola AJ, Hovy EH (2016) Hierarchical attention networks for document classification. In: HLT-NAACL, pp 1480–1489

  55. Zeinali H, BabaAli B, Hadian H (2018) Online signature verification using i-vector representation. IET Biom 7:405–414

    Article  Google Scholar 

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  1. School of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran

    Kian Ahrabian & Bagher BabaAli

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  1. Kian Ahrabian
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  2. Bagher BabaAli
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Correspondence to Bagher BabaAli.

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Ahrabian, K., BabaAli, B. Usage of autoencoders and Siamese networks for online handwritten signature verification. Neural Comput & Applic 31, 9321–9334 (2019). https://doi.org/10.1007/s00521-018-3844-z

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