Online Signature Verification Using Fully Connected Deep Neural Networks

Full Text (PDF, 933KB), PP.41-47

Views: 0 Downloads: 0


Snehal Reddy Yelmati 1,* Jayasree Hanumantha Rao 1

1. Department of Computer Science and Engineering, MVSR Engineering College, Hyderabad, Telangana, India

* Corresponding author.


Received: 8 May 2021 / Revised: 1 Jul. 2021 / Accepted: 26 Jul. 2021 / Published: 8 Oct. 2021

Index Terms

Online signature verification, Deep learning, Neural networks, SVC2004.


Biometric systems have been used in a wide range of applications. In this paper, we have introduced an online signature verification system using deep neural network models. The proposed system is designed to be used in a production environment and has accuracies on par with the state-of-the-art signature verification methods. It authenticates much faster than most of the existing signature verification systems (less than 2 seconds). To achieve better accuracies and faster training times, a feature vector with 42 features, both static and dynamic, is obtained from the signature sample. This feature vector is fed into the user identification model, which predicts the identity of the user with about 99% accuracy and based on this prediction, the user authentication model predicts if the signature is genuine or forged for that recognized user, with about 98% accuracy. The best possible accuracy achieved by the proposed system for 40 users is 97.5% and EER about 2%. The dataset from the Signature Verification Competition 2004 (SVC2004) was used to assess the performance of the proposed system. The results show that the proposed system competes with and even outperforms existing methods.

Cite This Paper

Snehal Reddy Yelmati, Jayasree Hanumantha Rao, " Online Signature Verification Using Fully Connected Deep Neural Networks ", International Journal of Engineering and Manufacturing (IJEM), Vol.11, No.5, pp. 41-47, 2021. DOI: 10.5815/ijem.2021.05.04


[1]National Research Council, Biometric Recognition. Washington, D.C.: National Academies Press, 2010.

[2]O. Miguel-Hurtado, L. Mengibar-Pozo, M. G. Lorenz, and J. Liu-Jimenez, “On-line signature verification by dynamic time warping and Gaussian mixture models,” Proc. - Int. Carnahan Conf. Secur. Technol., no. November, pp. 23–29, 2007, doi: 10.1109/CCST.2007.4373463.

[3]N. N. Liu and Y. H. Wang, “Fusion of global and local information for an on-line signature verification system,” Proc. 7th Int. Conf. Mach. Learn. Cybern. ICMLC, vol. 1, no. July, pp. 57–61, 2008, doi: 10.1109/ICMLC.2008.4620378.

[4]A. Kholmatov and B. Yanikoglu, “Identity authentication using improved online signature verification method,” Pattern Recognit. Lett., vol. 26, no. 15, pp. 2400–2408, Nov. 2005, doi: 10.1016/j.patrec.2005.04.017.

[5]L. Nanni, E. Maiorana, A. Lumini, and P. Campisi, “Combining local, regional and global matchers for a template protected on-line signature verification system,” Expert Syst. Appl., vol. 37, no. 5, pp. 3676–3684, May 2010, doi: 10.1016/j.eswa.2009.10.023.

[6]A. Sharma and S. Sundaram, “An enhanced contextual DTW based system for online signature verification using Vector Quantization,” Pattern Recognit. Lett., vol. 84, pp. 22–28, 2016, doi: 10.1016/j.patrec.2016.07.015.

[7]J. Fierrez-Aguilar, L. Nanni, J. Lopez-Peñalba, J. Ortega-Garcia, and D. Maltoni, “An On-Line Signature Verification System Based on Fusion of Local and Global Information,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3087, 2005, pp. 523–532.

[8]J. Fierrez, J. Ortega-Garcia, D. Ramos, and J. Gonzalez-Rodriguez, “HMM-based on-line signature verification: Feature extraction and signature modeling,” Pattern Recognit. Lett., vol. 28, no. 16, pp. 2325–2334, 2007, doi: 10.1016/j.patrec.2007.07.012.

[9]B. L. Van, S. Garcia-Salicetti, and B. Dorizzi, “On using the Viterbi path along with HMM likelihood information for online signature verification,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 37, no. 5, pp. 1237–1247, 2007, doi: 10.1109/TSMCB.2007.895323.

[10]S. Lai, L. Jin, and W. Yang, “Online Signature Verification Using Recurrent Neural Network and Length-Normalized Path Signature Descriptor,” Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, vol. 1, no. 1, pp. 400–405, 2017, doi: 10.1109/ICDAR.2017.73.

[11]A. Fallah, M. Jamaati, and A. Soleamani, “A new online signature verification system based on combining Mellin transform, MFCC and neural network,” Digit. Signal Process. A Rev. J., vol. 21, no. 2, pp. 404–416, 2011, doi: 10.1016/j.dsp.2010.09.004.

[12]S. Meshoul and M. Batouche, “A novel approach for online signature verification using fisher based probabilistic neural network,” Proc. - IEEE Symp. Comput. Commun., pp. 314–319, 2010, doi: 10.1109/ISCC.2010.5546760.

[13]D. Y. Yeung et al., “SVC2004: First international signature verification competition,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3072, pp. 16–22, 2004, doi: 10.1007/978-3-540-25948-0_3.

[14]“SVC2004 Competition Public Dataset,” 2004.

[15]S. Bhatia, P. Bhatia, D. Nagpal, and S. Nayak, “Online Signature Forgery Prevention,” Int. J. Comput. Appl., vol. 75, no. 13, pp. 21–29, Aug. 2013, doi: 10.5120/13172-0849.

[16]K. S. Manjunatha, S. Manjunath, D. S. Guru, and M. T. Somashekara, “Online signature verification based on writer dependent features and classifiers,” Pattern Recognit. Lett., vol. 80, pp. 129–136, 2016, doi: 10.1016/j.patrec.2016.06.016.

[17]U. Jaitley, “Why Data Normalization is necessary for Machine Learning models,” 2018. (accessed Aug. 12, 2020).

[18]D. Muramatsu and T. Matsumoto, “Effectiveness of pen pressure, azimuth, and altitude features for online signature verification,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4642 LNCS, pp. 503–512, 2007, doi: 10.1007/978-3-540-74549-5_53.

[19]M. Adamski and K. Saeed, “Online signature classification and its verification system,” Proc. - 7th Comput. Inf. Syst. Ind. Manag. Appl. CISIM 2008, no. 1, pp. 189–194, 2008, doi: 10.1109/CISIM.2008.38.

[20]S. Garcia-Salicetti et al., “Online Handwritten Signature Verification,” in Guide to Biometric Reference Systems and Performance Evaluation, London: Springer London, 2009, pp. 125–165.

[21]J. M. Pascual-Gaspar, V. Cardeñoso-Payo, and C. E. Vivaracho-Pascual, “Practical On-Line Signature Verification,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5558 LNCS, 2009, pp. 1180–1189.

[22]T. Hafs, L. Bennacer, A. Nait-Ali, M. Boughazi, and A. Bouzid-Daho, “Online signature verification approach using Mellin transform and empirical mode decomposition,” in 2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART), Aug. 2017, pp. 1–6, doi: 10.1109/BIOSMART.2017.8095311.