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A novel approach to validate online signature using dynamic features based on locally weighted learning

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Abstract

Online signature verification is most popular in the field of biometrics and forensics. Due to its popularity and recent demand, the major challenges are to improve its performance and complexity. This paper presents a novel approach for online signature validation based on local weight learning. The different features like, x coordinate, y coordinates time stamp, pen up and down, azimuth, height, pressure, displacement, velocity and acceleration are extracted from online signature. The extracted features are then passed to locally weighted learning classifier algorithms. Our experimentation is performed on local weight learning classifier of a machine learning method. Locally weighted learning classifier is experimentally found to be effective having False acceptance rate and False rejection rate as 1.18 and 0.02, respectively. The proposed methods gives better performance when compared with other well-known existing models for online signature verification. This result demonstrates that the method is suitable for a real-time system.The popular SVC2004 dataset is used in the experiments, which confirms the effectiveness of the proposed method in simultaneously achieving lower false positive and false negative rate.

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References

  1. Chandra S (2020) Verification of dynamic signature using machine learning approach. Neural Comput Appl:1–21

  2. Chandra S, Singh KK, Kumar S, Ganesh K, Sravya L, Kumar BP (2021) A novel approach to validate online signature using machine learning based on dynamic features. Neural Comput Appl:1–20

  3. Diaz M, Ferrer MA, Impedovo D, Malik MI, Pirlo G, Plamondon R (2019) A perspective analysis of handwritten signature technology. ACM Comput Surveys (CSUR) 51(6):1–39

    Article  Google Scholar 

  4. Englert P (2012) Locally weighted learning. In: Seminar class on autonomous learning systems. Citeseer

  5. Ferrer MA, Diaz-Cabrera M, Morales A (2013) Synthetic off-line signature image generation. In: 2013 International conference on biometrics (ICB), pp 1–7. IEEE

  6. Fierrez J, Ortega-Garcia J, Ramos D, Gonzalez-Rodriguez J (2007) Hmm-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn Lett 28(16):2325–2334

    Article  Google Scholar 

  7. Hafs T, Bennacer L, Boughazi M, Nait-Ali A (2016) Empirical mode decomposition for online handwritten signature verification. IET Biometrics 5(3):190–199

    Article  Google Scholar 

  8. Hussain L, Aziz W, Nadeem S, Abbasi A (2014) Classification of normal and pathological heart signal variability using machine learning techniques. Int J Darshan Inst Eng Res Emerg Technol 3(2):13–18

    Google Scholar 

  9. Impedovo D, Pirlo G (2008) Automatic signature verification: the state of the art. IEEE Trans Syst Man Cybern Part C (Applications and Reviews) 38 (5):609–635

    Article  Google Scholar 

  10. Kumar MM, Puhan NB (2014) Off-line signature verification: upper and lower envelope shape analysis using chord moments. IET Biometrics 3(4):347–354

    Article  Google Scholar 

  11. Okawa M (2019) Template matching using time-series averaging and dtw with dependent warping for online signature verification. IEEE Access 7:81,010–81,019

    Article  Google Scholar 

  12. Oliveira LS, Justino E, Sabourin R (2007) Off-line signature verification using writer-independent approach. In: 2007 International joint conference on neural networks, pp 2539–2544. IEEE

  13. Parziale A, Diaz M, Ferrer MA, Marcelli A (2019) Sm-dtw: stability modulated dynamic time warping for signature verification. Pattern Recogn Lett 121:113–122

    Article  Google Scholar 

  14. Pramanik R, Bag S (2020) Segmentation-based recognition system for handwritten bangla and devanagari words using conventional classification and transfer learning. IET Image Process 14(5):959–972

    Article  Google Scholar 

  15. Riesen K, Schmidt R (2019) Online signature verification based on string edit distance. Int J Document Anal Recogn (IJDAR) 22(1):41–54

    Article  Google Scholar 

  16. Schaal S, Atkeson CG, Vijayakumar S (2000) Real-time robot learning with locally weighted statistical learning. In: Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), vol 1, pp 288–293. IEEE

  17. Schneider J, Moore AW (2000) A locally weighted learning tutorial using vizier 1.0, vol. 149. Carnegie Mellon University the Robotics Institute

  18. Shuai M, Xie K, Pu W, Song G, Ma X (2008) An online approach based on locally weighted learning for short-term traffic flow prediction. In: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, pp 1–4

  19. Tan H, He L, Huang ZC, Zhan H (2021) Online signature verification based on dynamic features from gene expression programming. Multimed Tools Appl:1–27

  20. Vorugunti CS, Pulabaigari V, Gorthi RKSS, Mukherjee P (2020) Osvfusenet: online signature verification by feature fusion and depth-wise separable convolution based deep learning. Neurocomputing 409:157–172

    Article  Google Scholar 

  21. Wu X, Kimura A, Iwana BK, Uchida S, Kashino K (2019) Deep dynamic time warping: end-to-end local representation learning for online signature verification. In: 2019 International conference on document analysis and recognition (ICDAR), pp 1103–1110. IEEE

  22. Yahyatabar ME, Baleghi Y, Karami MR (2015) Online signature verification: a robust approach for persian signatures

  23. Yahyatabar ME, Ghasemi J (2017) Online signature verification using double-stage feature extraction modelled by dynamic feature stability experiment. IET Biometrics 6(6):393–401

    Article  Google Scholar 

  24. Yang L, Cheng Y, Wang X, Liu Q (2018) Online handwritten signature verification using feature weighting algorithm relief. Soft Comput 22 (23):7811–7823

    Article  Google Scholar 

  25. Yeung DY, Chang H, Xiong Y, George S, Kashi R, Matsumoto T, Rigoll G (2004) Svc2004: first international signature verification competition. In: International conference on biometric authentication, pp 16–22. Springer

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India

    Subhash Chandra

  2. Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, Jharkhand, India

    Vinay Kumar

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  1. Subhash Chandra
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  2. Vinay Kumar
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Correspondence to Subhash Chandra.

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Chandra, S., Kumar, V. A novel approach to validate online signature using dynamic features based on locally weighted learning. Multimed Tools Appl 81, 40959–40976 (2022). https://doi.org/10.1007/s11042-022-13159-6

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