Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters





Signature verification, k-nearest neighbor, classification


In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) classifier for online signature verification. After studying the algorithm's main parameters, we use four separate databases to present and evaluate each algorithm parameter. The results show that the proposed method can increase the verification accuracy by 0.73-10% compared to a traditional one class k-NN classifier. The algorithm has achieved reasonable accuracy for different databases, a 3.93% error rate when using the SVC2004 database, 2.6% for MCYT-100 database, 1.75% for the SigComp'11 database, and 6% for the SigComp'15 database.

The proposed algorithm uses specifically chosen parameters and a procedure to pick the optimal value for K using only the signer's reference signatures, to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error achieved was 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp'15, and 2.22% for SigComp'11.


Download data is not yet available.

Author Biographies

Mohammad Saleem, Budapest University of Technology and Economics

Mohammad Saleem is a Ph.D. candidate in software engineering at Budapest University of Technology and Economics, Hungary, at the Department of Automation and Applied Informatics. His research interests include Online signature verification. He worked as a researcher and teacher assistant at Yarmouk University, Irbid, Jordan, and Budapest University of Technology and Economics. He is a member of the Jordanian engineers association.

Bence Kovari, Budapest University of Technology and Economics

Bence Kovari received a Ph.D. degree in software engineering from Budapest University of Technology and Economics, Hungary in 2013. He has been working there since then as a researcher and teacher, currently as an associate professor at the Department of Automation and Applied Informatics. His research interests include software engineering and the automated verification of handwritten signatures. He has over 50 publications in the field. He is a member of the Hungarian Association for Image Processing and Pattern Recognition, a member society of the John von Neumann Computer Society.


Abdelrahaman AA, Abdallah MA (2013). Knearest neighbor classifier for signature verification system. In: 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE). IEEE.

Azmi AN, Nasien D, Omar FS (2017). Biometric signature verification system based on freeman chain code and k-nearest neighbor. Multimedia Tools and Applications 76:15341–55.

Cabral GG, Oliveira AL, Cahu CB (2009). Combining ´ nearest neighbor data description and structural risk minimization for one-class classification. Neural Computing and Applications 18:175–83.

Cover T, Hart P (1967). Nearest neighbor pattern classification. IEEE transactions on information theory 13:21–7.

Gao L, Zhang L, Liu C, Wu S (2020). Handling imbalanced medical image data: A deep-learningbased one-class classification approach. Artificial Intelligence in Medicine 108:101935. 6 Image Anal Stereol ?? (Please use volume):1-7

Goldin DQ, Kanellakis PC (1995). On similarity queries for time-series data: constraint specification and implementation. In: International Conference on Principles and Practice of Constraint Programming. Springer.

Guru D, Prakash H (2008). Online signature verification and recognition: An approach based on symbolic representation. IEEE transactions on pattern analysis and machine intelligence 31:1059–73.

Harfiya LN, Widodo AW, Wihandika RC (2017). Offline signature verification based on pyramid histogram of oriented gradient features. In: 2017 1st International Conference on Informatics and Computational Sciences (ICICoS). IEEE.

Isha I, Pooja P, Varsha V (2016). Offline signature verification based on euclidean distance using support vector machine. International Journal of Advanced Engineering Management and Science 2:239595.

Jain A, Singh SK, Singh KP (2020). Handwritten signature verification using shallow convolutional neural network. Multimedia Tools and Applications :1–26.

Khan SS, Ahmad A (2018). Relationship between variants of one-class nearest neighbors and creating their accurate ensembles. IEEE Transactions on Knowledge and Data Engineering 30:1796–809.

Kholmatov A, Yanikoglu B (2009). Susig: an on-line signature database, associated protocols and benchmark results. Pattern Analysis and Applications 12:227–36.

KOMIYA Y, Ohishi T, Matsumoto T (2001). A pen input on-line signature verifier integrating position, pressure and inclination trajectories. IEICE transactions on information and systems 84:833– 8.

Liu Y, Yang Z, Yang L (2014). Online signature verification based on dct and sparse representation. IEEE transactions on cybernetics 45:2498–511.

Liwicki M, Malik MI, Van Den Heuvel CE, Chen X, Berger C, Stoel R, Blumenstein M, Found B (2011). Signature verification competition for online and offline skilled forgeries (sigcomp2011). In: 2011 International Conference on Document Analysis and Recognition. IEEE.

Malallah FL, Ahmad SMS, Adnan WAW, Arigbabu OA, Iranmanesh V, Yussof S (2015). Online handwritten signature recognition by length normalization using up-sampling and downsampling. International Journal of Cyber Security and Digital Forensics IJCSDF 4:302–13.

Malik MI, Ahmed S, Marcelli A, Pal U, Blumenstein M, Alewijns L, Liwicki M (2015). Icdar2015 competition on signature verification and writer identification for on-and off-line skilled forgeries (sigwicomp2015). In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE.

Manevitz LM, Yousef M (2001). One-class svms for document classification. Journal of machine Learning research 2:139–54.

Nanni L (2006). Experimental comparison of oneclass classifiers for online signature verification. Neurocomputing 69:869–73.

Okawa M (2020). Online signature verification using single-template matching with time-series averaging and gradient boosting. Pattern Recognition 102:107227.

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. (2003). Mcyt baseline corpus: a bimodal biometric database. IEE Proceedings Vision Image and Signal Processing 150:395–401. Pippin CE (2004). Dynamic signature verification using local and global features. Georgia Institute of Technology .

Rodr´ıguez-Ruiz J, Mata-Sanchez JI, Monroy R, ´ Loyola-Gonzalez O, L ´ opez-Cuevas A (2020). A ´ one-class classification approach for bot detection on twitter. Computers Security 91:101715.

Tolosana R, Vera-Rodriguez R, Ortega-Garcia J, Fierrez J (2015). Preprocessing and feature selection for improved sensor interoperability in online biometric signature verification. IEEE Access 3:478–89.

Vargas JF, Ferrer MA, Travieso CM, Alonso JB (2008). Off-line signature verification based on high pressure polar distribution. In: Procedeeins of the 11th International Conference on Frontiers in Handwriting Recognition, ICFHR 2008.

Vickram P, Swapna ASKD (2016). Offline signature verification using support local binary pattern. In: International Journal of Artificial Intelligence and Applications (IJAIA).

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

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. Springer.




How to Cite

Saleem, M., & Kovari, B. (2021). Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters. Computer Science, 22(4). https://doi.org/10.7494/csci.2021.22.4.4102