Skip to main content
Springer Nature Link
Log in

Verification of genuine and forged offline signatures using Siamese Neural Network (SNN)

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Deep learning plays very important role in almost all domains and application areas of AI like computer vision, biometrics, NLP, Healthcare etc. However, when there is lack of training data then it is difficult to train a model. Siamese is one of the popular network in deep learning and applications. This network architecture is composed of two or more identical sub-network component and also shares same weights among them. The core benefit of this network is, it can learn from one input image along with one target image. Further it is identified as one shot learning. Generally, to avert falsification and discriminate geunine as well as forged signature, Convolutional Neural Network (CNN) is highly researched. The proposed work represents Siamese neural network using Convolutional Neural Network as a subnetwork for the proposed system. In Siamese network embedding vector is generated and to make this vector more robust, we proposed to add some statistical measures to it, which are calculated on embedding vector itself. Lastly contrastive loss function is applied to resultant embedding vector. The proposed network surpass the state-of-the-art results in terms of accuracy, FAR and FRR.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Alonso-Fernandez F, Fairhurst MC, Fierrez J, Ortega-Garcia J (2007) Automatic measures for predicting performance in off-line signature. In: Image processing, 2007. ICIP 2007. IEEE international conference on, vol 1, pp I–369

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

    Article  Google Scholar 

  3. Bharathi R, Shekar B (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), IEEE, pp 2063–2068

  4. Bhattacharya I, Ghosh P, Biswas S (2013) Offline signature verification using pixel matching technique. Procedia Technol 10:970–977

    Article  Google Scholar 

  5. Bhunia A K, Alaei A, Roy PP (2019) Signature verification approach using fusion of hybrid texture features. Neural Comput Applic pp 1–12

  6. Boyko A, Rozorynov G (2010) Signature based authentication

  7. 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

  8. Das S, Roy A (2016) Signature verification using rough set theory based feature selection. In: Computational intelligence in data mining, vol 1. Springer, New York, pp 153–161

  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:1707.02131

  10. Dutta A, Pal U, Lladós J (2016) Compact correlated features for writer independent signature verification. In: 23Rd international conference on pattern recognition (ICPR), IEEE, pp 3422–3427

  11. Ferrer MA, Diaz-Cabrera M, Morales A, et al (2013) Synthetic off-line signature image generation. In: 6th IAPR international conference on biometrics (ICB), pp 1–7

  12. Ghosh R, Kumar P, Roy PP (2019) A dempster–shafer theory based classifier combination for online signature recognition and verification systems. Int J Mach Learn Cybern 10(9):2467–2482

    Article  Google Scholar 

  13. Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. IEEE, pp 1735–1742

  14. Hafemann LG, Sabourin R, Oliveira LS (2016) Writer-independent feature learning for offline signature verification using deep convolutional neural networks. In: International joint conference on neural networks (IJCNN), IEEE, pp 2576–2583

  15. Hezil H, Djemili R, Bourouba H (2018) Signature recognition using binary features and KNN. Int J Biom 10(1):1–15

    Google Scholar 

  16. Jagtap AB, Hegadi RS (2017) Offline handwritten signature recognition based on upper and lower envelope using eigen values. In: World congress on computing and communication technologies (WCCCT), IEEE, pp 223–226

  17. Jagtap AB, Hegadi RS (2018) Siamese network for learning genuine and forged offline signature verification. In: International conference on recent trends in image processing and pattern recognition. Springer

  18. Jagtap AB, Hegadi RS, Feature learning for offline handwritten signature verification using convolution neural networkl. International Journal of Technology and Human Interaction (IJTHI) IGI Global (Inpress)

  19. Justino E J, Bortolozzi F, Sabourin R (2001) Off-line signature verification using hmm for random, simple and skilled forgeries. In: Sixth international conference on document analysis and recognition, IEEE, pp 1031–1034

  20. Justino EJ, Bortolozzi F, Sabourin R (2001) Off-line signature verification using HMM for random, simple and skilled forgeries Sixth international conference on document analysis and recognition, IEEE, 1031–1034

  21. Kalera MK, Srihari S, Xu A (2004) Offline signature verification and identification using distance statistics. International Journal of Pattern Recognition and Artificial Intelligence (07):1339–1360

  22. Kiani V, Pourreza Shahri R, Pourreza HR (2009) Offline signature verification using local radon transform and support vector machines. International Journal of Image Processing 3

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

  24. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

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

    Article  Google Scholar 

  26. Majhi B, Reddy YS, Babu DP (2006) Novel features for off-line signature verification. Int J Comput Commun Control 1(1):17–24

    Article  Google Scholar 

  27. Manjunatha K, Manjunath S, Guru D, Somashekara M (2016) Online signature verification based on writer dependent features and classifiers. Pattern Recogn Lett 80:129–136

    Article  Google Scholar 

  28. Nguyen V, Blumenstein M (2011) An application of the 2d gaussian filter for enhancing feature extraction in off-line signature verification. In: International conference on document analysis and recognition, IEEE, pp 339–343

  29. Nguyen V, Blumenstein M, Muthukkumarasamy V, Leedham G (2007) Off-line signature verification using enhanced modified direction features in conjunction with neural classifiers and support vector machines. In: Ninth international conference on document analysis and recognition, ICDAR, IEEE, vol 2, pp 734–738

  30. Ooi SY, Teoh ABJ, Pang YH, Hiew BY (2016) 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

    Article  Google Scholar 

  31. Ortega-Garcia J, Fierrez-Aguilar J, Simon D, Gonzalez J, Faundez-Zanuy M, Espinosa V, Satue A, Hernaez I, Igarza JJ, Vivaracho C et al Mcyt baseline corpus: a bimodal biometric database. IEE Proceedings-Vision, Image and Signal Processing

  32. Radhika K, Venkatesha M, Sekhar G (2011) An approach for on-line signature authentication using zernike moments. Pattern Recogn Lett 32(5):749–760

    Article  Google Scholar 

  33. Roy A, Devi SS, Laskar R (2016) Impulse noise removal from gray scale images based on ann classification based fuzzy filter. In: 2016 2nd international conference on computational intelligence and networks (CINE), IEEE, pp 97–101

  34. Soleimani A, Araabi BN, Fouladi K (2016) Deep multitask metric learning for offline signature verification. Pattern Recogn Lett 80:84–90

    Article  Google Scholar 

  35. Vargas JF, Ferrer MA, Travieso C, Alonso JB (2011) Off-line signature verification based on grey level information using texture features. Pattern Recogn 44 (2):375–385

    Article  Google Scholar 

Download references

Acknowledgments

Authors thank to the Ministry of Electronics and Information Technology (MeitY), New Delhi for granting Visvesvaraya Ph.D. fellowship through file no. PhD-MLA∖4(34)∖2015-16 Dated: 5/11/2015.

Author information

Authors and Affiliations

  1. Department of Computer Science, Solapur University, Solapur, 413255, Maharashtra, India

    Amruta B. Jagtap, Dattatray D. Sawat & Ravindra S. Hegadi

  2. Department of Computer Science, Indian Institute of Information Technology, Dharwad, 580029, Karnataka, India

    Rajendra S. Hegadi

  3. Department of Computer Science, Central University of Karnataka, Kadaganchi, Kalaburagi, 585367, India

    Ravindra S. Hegadi

Authors
  1. Amruta B. Jagtap
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dattatray D. Sawat
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Rajendra S. Hegadi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ravindra S. Hegadi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Amruta B. Jagtap.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jagtap, A.B., Sawat, D.D., Hegadi, R.S. et al. Verification of genuine and forged offline signatures using Siamese Neural Network (SNN). Multimed Tools Appl 79, 35109–35123 (2020). https://doi.org/10.1007/s11042-020-08857-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08857-y

Keywords