Skip to main content
SpringerLink
Log in

COMPOSV++: Light Weight Online Signature Verification Framework Through Compound Feature Extraction and Few-Shot Learning

  • Conference paper
  • First Online:
Frontiers in Handwriting Recognition (ICFHR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13639))

Included in the following conference series:

  • 1043 Accesses

Abstract

Online Signature Verification (OSV) is a systematically used biometric characteristic to endorse the genuineness of a user to access real time applications like healthcare, m-payment, etc. Because OSV frameworks are used in real-time applications and it is difficult to acquire a sufficient number of signature samples from users, they must meet a critical requirement: they must be able to detect skilled and random signature presentation attacks effectively with fewer training signature samples and a faster response time. To meet these needs, we developed a depth wise separable (DWS) convolution-based OSV framework that realizes one/few shot learning in inference phase. In addition to it, we have designed a compound feature extraction technique, which extracts maximum seven features from a set of 100 features in MCYT-100, and 3 features from a set of 47 in case of {SVC, SUSIG} datasets. The framework uses only three to seven features per signature to resist the signature presentation attacks. We have extensively evaluated our framework, by performing thorough experiments with three datasets i.e. MCYT-100, SVC and SUSIG. The model results state of the art EER in all skilled categories of SVC and SUSIG datasets.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Devanur, G., Koushik, M., Manjunath, S., Somashekara, M.: Interval valued symbolic representation of writer dependent features for online signature verification. Elsevier J. Expert Syst. Appl. 80, 232–243 (2017)

    Article  Google Scholar 

  2. Sekhar, C., Sai, G., Viswanath, P.: Online signature verification by few-shot separable convolution based deep learning. In: 15th International Conference on Document Analysis and Recognition (ICDAR 2019) Sydney, Australia, pp. 1125–1129 (2019)

    Google Scholar 

  3. Vorugunti, S., Anoushka, D., Prerana, M., Viswanath, P.: A light weight and hybrid deep learning model based online signature verification. In: ICDAR WML 2019 2nd International Workshop on Machine Learning, 2019, pp. 53–59 (2019)

    Google Scholar 

  4. Koushik, M., Shantharamu, M., Devanur, G., Somashekara, M.T.: Online signature verification based on writer dependent features and classifiers. Pattern Recogn. Lett. 80, 129–136 (2016)

    Article  Google Scholar 

  5. Chandra, V., Devanur, G., Pulabaigari, V.: An efficient online signature verification based on feature fusion and interval valued representation of writer dependent features. In: IEEE 5th International Conference on Identity, Security and Behavior Analysis (ISBA) (2019)

    Google Scholar 

  6. Emanuele, M., Patrizio, C., Julian, F., Javier, O., Alessandro, N.: Cancelable templates for sequence-based biometrics with application to on-line signature recognition. In: IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 40, no. 3, pp. 525–538 (2010)

    Google Scholar 

  7. Moises, D., Andreas, F., Miguel, A.F., Réjean, P.: Dynamic signature verification system based on one real signature. In: IEEE Transactions on Cybernetics, vol. 48 (2018)

    Google Scholar 

  8. Abdul, A., Madasu, H., Jaspreet, K., Abhineet, S.: Online signature verification using segment-level fuzzy modelling. IET Biometrics 3(3), 113–127 (2014)

    Article  Google Scholar 

  9. Lei, T., Wenxiong, K., Yuxun, F.: Information divergence-based matching strategy for online signature verification. In: IEEE Transactions on Information Forensics and Security, vol. 13 (2018)

    Google Scholar 

  10. Abhishek, S., Suresh, S.: On the exploration of information from the DTW cost matrix for online signature verification. In: IEEE Transactions on Cybernetics, vol 48 (2018)

    Google Scholar 

  11. Sae-Bae, N., Nasir, M.: Online signature verification on mobile devices. In. Transactions on Information Forensics and Security, vol. 9, no. 6, pp. 933–947 (2014)

    Google Scholar 

  12. Abhishek, S., Suresh, S.: An enhanced contextual DTW based system for online signature verification using vector quantization. Pattern Recogn. Lett. 84, 22–28 (2016)

    Article  Google Scholar 

  13. Chandra Sekhar, V., Devanur, G., Prerana, M., Viswanath, P.: OSVNet: convolutional siamese network for writer independent online signature verification. In: 15th ICDAR, Sydney, Australia, pp. 1470–1475 (2019)

    Google Scholar 

  14. Rafal, D., Przemyslaw, K., Piotr, P.: Online signature verification modeled by stability oriented reference signatures. Inf. Sci. 460–461, 151–171 (2018)

    Google Scholar 

  15. Rami, A., Witold, P., Khaled, D., Ali, M., Ahmed, A.L.: Quantifying dynamic time warping distance using probabilistic model in verification of dynamic signatures. Elsevier-Soft Comput. vol. 23, pp. 407–418 (2019)

    Google Scholar 

  16. He, L., Tan, H., Huang, Z.-C.: Online handwritten signature verification based on association of curvature and torsion feature with Hausdorff distance. Multimedia Tools Appl. 78(14), 19253–19278 (2019). https://doi.org/10.1007/s11042-019-7264-6

    Article  Google Scholar 

  17. Devanur, G., Prakash, H.N.: Online signature verification and recognition: an approach based on symbolic representation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31 (2009)

    Google Scholar 

  18. Biswajit, K., Anirban, M., Pranab, K.: Stroke point warping-based reference selection and verification of online signature. In: IEEE Transactions on Instrumentation and Measurement, vol. 67 (2018)

    Google Scholar 

  19. Yang, L., Cheng, Y., Wang, X., Liu, Q.: Online handwritten signature verification using feature weighting algorithm relief. Soft. Comput. 22(23), 7811–7823 (2018). https://doi.org/10.1007/s00500-018-3477-2

    Article  Google Scholar 

  20. Songxuan, L., Lianwen, J., Weixin, Y.: Online signature verification using recurrent neural network and length-normalized path signature descriptor. In:14th ICDAR (2017)

    Google Scholar 

  21. Mostafa, I., Mohamed, M., Hazem, M.: Enhanced DTW based on-line signature verification. In: Proceedings of the 16th IEEE International Conference on Image Processing (ICIP) (2009)

    Google Scholar 

  22. Saeid, R., Ali, F., Farzad, T.: Authentication based on pole-zero models of signature velocity. J. Med. Signals Sens. vol 3, pp.195–208 (2013)

    Google Scholar 

  23. Diaz, M., Andreas, F., Réjean, P., Miguel, F.: Towards an automatic on-line signature verifier using only one reference per signer. In: International Conference on Document Analysis and Recognition (ICDAR), Tunis, Tunisia, pp. 631–635 (2015)

    Google Scholar 

  24. Alireza, A., Srikanta, P., Umapada, P., Michael, B.: An efficient signature verification method based on an interval symbolic representation and a fuzzy similarity measure. In: IEEE Transactions on Information Forensics and Security, vol. 12 (2017)

    Google Scholar 

  25. Javier, G., Julian, F., Marcos, M., Javier, O.: Improving the enrollment in dynamic signature verfication with synthetic samples. In: ICDAR, pp. 1295–1299, Barcelona, Spain (2009)

    Google Scholar 

  26. Ruben, T., Ruben, V., Julian, F., Javier, O.: Biometric signature verification using recurrent neural networks. In: 14th ICDAR, Kyoto, Japan (2017)

    Google Scholar 

  27. Lukasz, K., Aidan, G., Francois, C.: Depthwise separable convolutions for neural machine translation. In: 6th International Conference on Learning Representations (ICLR) (2018)

    Google Scholar 

  28. Francois, C.: Xception: deep learning with depthwise separable convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, pp:1251–1258 (2017)

    Google Scholar 

  29. Sreevani, Murthy, C.A.: Bridging feature selection and extraction compound feature generation. In: IEEE Transactions on Knowledge and Data Engineering, vol. 29, pp: 757–770 (2017)

    Google Scholar 

  30. Rohit, K., Richa, S., Mayank, V.: Guided dropout. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp 4065–4072 (2019)

    Google Scholar 

  31. Chuang, L., Xing, Z., Feng, L.: A stroke-based RNN for writer-independent online signature verification. In: 15th ICDAR, pp. 526–532 (2019)

    Google Scholar 

  32. Chandra, S., Prerana, M., Devanur, G., Viswanath, P.: Online signature verification based on writer specific feature selection and fuzzy similarity measure. In: Workshop on Media Forensics, CVPR 2019, Long Beach, USA, pp. 88–95 (2019)

    Google Scholar 

  33. Antonio, P., Moises, D., Miguel, A., Angelo, M.: SM-DTW: Stability modulated dynamic time warping for signature verification. PRL, vol. 121, pp. 113–122. 15 April 2019

    Google Scholar 

  34. Javier, G., Julian. F., Marcos, M., Javier, O.: Improving the enrollment in dynamic signature verificationwith synthetic sample. In: ICDAR, Tunis, Tunisia, pp. 1295–1299 (2015)

    Google Scholar 

  35. Vorugunti, C.S., Pulabaigari, V., Mukherjee, P., et al.: COMPOSV: compound feature extraction and depthwise separable convolution-based online signature verification. Neural Comput. Appl. 34, 10901–10928 (2022). https://doi.org/10.1007/s00521-022-07018-6

    Article  Google Scholar 

  36. Bhowal, P., Banerjee, D., Malakar, S., et al.: A two-tier ensemble approach for writer dependent online signature verification. J. Ambient Intell. Hum. Comput. 13, 21–40 (2022). https://doi.org/10.1007/s12652-020-02872-5

    Article  Google Scholar 

  37. Chandra, S.: Verification of dynamic signature using machine learning approach. Neural Comput. Appl. 32(15), 11875–11895 (2020). https://doi.org/10.1007/s00521-019-04669-w

    Article  Google Scholar 

  38. Chandra, S., Singh, K.K., Kumar, S., Ganesh, K.V.K.S., Sravya, L., Kumar, B.P.: A novel approach to validate online signature using machine learning based on dynamic features. Neural Comput. Appl. 33(19), 12347–12366 (2021). https://doi.org/10.1007/s00521-021-05838-6

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology, SriCity, 517646, Andhra Pradesh, India

    Chandra Sekhar Vorugunti

  2. Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, 515134, Andhra Pradesh, India

    Balasubramanian Subramanian

  3. Jawaharlal Nehru University, New Delhi, 110067, India

    Prerana Mukherjee

  4. Birla Institute of Technology and Science, Pilani, 333031, Rajasthan, India

    Avinash Gautam

Authors
  1. Chandra Sekhar Vorugunti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Balasubramanian Subramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prerana Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Avinash Gautam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chandra Sekhar Vorugunti .

Editor information

Editors and Affiliations

  1. Walmart Inc., Hoboken, NJ, USA

    Utkarsh Porwal

  2. Universitat Autònoma de Barcelona, Barcelona, Spain

    Alicia Fornés

  3. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Faisal Shafait

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vorugunti, C.S., Subramanian, B., Mukherjee, P., Gautam, A. (2022). COMPOSV++: Light Weight Online Signature Verification Framework Through Compound Feature Extraction and Few-Shot Learning. In: Porwal, U., Fornés, A., Shafait, F. (eds) Frontiers in Handwriting Recognition. ICFHR 2022. Lecture Notes in Computer Science, vol 13639. Springer, Cham. https://doi.org/10.1007/978-3-031-21648-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21648-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21647-3

  • Online ISBN: 978-3-031-21648-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation