On-Line Signature Partitioning Using a Population Based Algorithm

Open access


The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1] Cpałka K. Design of Interpretable Fuzzy Systems. Springer Cham (2017)

  • [2] Cpałka K. Zalasiński M. On-line signature verification using vertical signature partitioning Expert Systems with Applications vol. 41 pp. 4170-4180 (2014)

  • [3] Cpałka K. Zalasiński M. Rutkowski L. A new algorithm for identity verification based on the analysis of a handwritten dynamic signature Applied Soft Computing vol. 43 pp. 47-56 (2016)

  • [4] Cpałka K. Zalasiński M. Rutkowski L. New method for the on-line signature verification based on horizontal partitioning Pattern Recognition vol. 47 pp. 2652-2661 (2014)

  • [5] Das S. Suganthan P.N. Differential evolution: A survey of the state-of-the-art. IEEE transactions on evolutionary computation vol. 15(1) pp. 4-31 (2010)

  • [6] Dean D. Sridharan S. Dynamic visual features for audio-visual speaker verification Comput. Speech Lang. vol. 24 pp. 136–149 (2010)

  • [7] Ekinci M. Ayku M. Human gait recognition based on kernel PCA using projections J. Comput. Sci. Technol. vol. 22 pp. 867–876 (2007)

  • [8] Faundez-Zanuy M. On-line signature recognition based on VQ-DTW. Pattern Recogn. 40 981-992 (2007)

  • [9] Fierrez-Aguilar J. Nanni L. Lopez-Penalba J. Ortega-Garcia J. Maltoni D. An on-line signature verification system based on fusion of local and global information. Lecture Notes in Computer Science. Audio-and Video-based Biometric Person Authentication vol. 3546 pp. 523-532 (2005)

  • [10] Fierrez J. Ortega-Garcia J. Ramos D. Gonzalez-Rodriguez J. HMM–based on-line signature verification: Feature extraction and signature modeling Pattern Recognition Letters vol. 28 pp. 2325–2334 (2007)

  • [11] Homepage of Association BioSecure. [Online] Available from: http://biosecure.it-sudparis.eu [Accessed: 13 May 2019]

  • [12] Houmani N. Mayoue A. Garcia-Salicetti S. Dorizzi B. Khalil M.I. Moustafa M.N. Abbas H. Muramatsu D. Yanikoglu B. Kholmatov A. Martinez-Diaz M. Fierrez J. Ortega-Garcia J. Roure Alcobe J. Fabregas J. Faundez-Zanuy M. Pascual-Gaspar J.M. Cardenoso-Payo V. Vivaracho-Pascual C. BioSecure signature evaluation campaign (BSEC’2009): Evaluating online signature algorithms depending on the quality of signatures Pattern Recognition vol. 45 pp. 993-1003 (2012)

  • [13] Ibrahim M.T. Khan M.A. Alimgeer K.S. Khan M.K. Taj I.A. Guan L. Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification. Pattern Recogn. 43 2817-2832 (2010)

  • [14] Jain A.K. Ross A. Introduction to Biometrics. In A.K. Jain P. Flynn A.A. Ross (Eds.) Handbook of Biometrics Springer Berlin-Heidelberg (2008)

  • [15] Kazikova A. Pluhacek M. Senkerik R. Viktorin A. Proposal of a new swarm optimization method inspired in bison behavior. In 23rd International Conference on Soft Computing pp. 146-156 Springer Cham (2017)

  • [16] Linden J. Marquis R. Bozza S. Taroni F. Dynamic signatures: A review of dynamic feature variation and forensic methodology Forensic Science International vol. 291 pp. 216-229 (2018)

  • [17] Łapa K. Meta-optimization of multi-objective population-based algorithms using multi-objective performance metrics Information Sciences vol. 489 pp. 193-204 (2019)

  • [18] Mirjalili S. Mirjalili S.M. Lewis A. Grey wolf optimizer. Advances in engineering software vol. 69 pp. 46-61 (2014)

  • [19] Nanni L. Maiorana E. Lumini A. and Campisi P. Combining local regional and global matchers for a template protected on-line signature verification system. Expert Systems with Applications 37 3676-3684 (2010)

  • [20] Pedersen M.E.H. Good parameters for differential evolution. Hvass Laboratories Technical Report vol. HL1002 (2010)

  • [21] Prasad M. Liu Y.T. Li D.L. Lin Ch.T. Shah R.R. Kaiwartya O.P. A New Mechanism for Data Visualization with TSK-type Preprocessed Collaborative Fuzzy Rule based System Journal of Artificial Intelligence and Soft Computing Research vol. 7 33–46 (2017)

  • [22] Riid A. Preden J.S. Design of fuzzy rule-based classifiers through granulation and consolidation Journal of Artificial Intelligence and Soft Computing Research vol. 7 pp. 137-147 (2017)

  • [23] Zalasinśki M. Łapa K. Cpałka K. Prediction of values of the dynamic signature features Expert Systems with Appications vol. 104 pp. 86-96 (2018)

  • [24] Zalasinśki M. Cpałka K. A Method for Genetic Selection of the Dynamic Signature Global Features’ Subset Advances in Intelligent Systems and Computing vol. 655 pp. 73-82 (2018)

  • [25] Zois E.N. Alexandridis A. Economou G. Writer independent offline signature verification based on a symmetric pixel relations and unrelated training-testing data sets Expert Systems With Applications vol. 125 pp. 14-32 (2019)

Journal information
Impact Factor

CiteScore 2018: 4.70

SCImago Journal Rank (SJR) 2018: 0.351
Source Normalized Impact per Paper (SNIP) 2018: 4.066

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 61 61 45
PDF Downloads 70 70 50