Abstract
This paper presents an innovative approach to solve the on-line signature verification problem in the presence of skilled forgeries. Genetic algorithms (GA) and fuzzy reasoning are the core of our solution. A standard GA is used to find a near optimal representation of the features of a signature to minimize the risk of accepting skilled forgeries. Fuzzy reasoning here is carried out by Neural Networks. The method of a human expert examiner of questioned signatures is adopted here. The solution was tested in the presence of genuine, random and skilled forgeries, with high correct verification rates.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Plamondon, R., Lorette, G.: Automatic signature verification and writer identification – The state of the art. Pattern Recognition 22, 107–131 (1989)
Dimauro, G., Impedovo, S.: Recent Advancements in Automatic Signature Verification. In: 9th Int’l Workshop on Frontiers in Handwriting Recognition. IEEE CS, Los Alamitos (2004)
Yampolskiy, R.V.: Motor-Skill Based Biometrics. In: 6th Annual Security Conference 2007, pp. 33-1 – 33-12 (2007)
Plamondon, R.: The design of an on-line signature verification system: from theory to practice. In: Progress in Automatic Signature Verification, pp. 795–811 (1994)
Plamondon, R., Srihari, S.N.: On-line and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 63–84 (2000)
Plamondon, R., Lorette, G.: On-line signature verification: how many countries are in the race? In: International Carnahan Conference on Security Technology, pp. 183–191 (1989)
Nalwa, S.: Automatic online signature verification. Proceedings of the IEEE 85(2), 213–239 (1997)
Ketabdar, H., Richiardi, J., Drygajlo, A.: Global feature selection for on-line signature verification. In: 12th International Graphonomics Society Conference (2005)
Ketabdar, H., Richiardi, J., Drygajlo, A.: Global and Local Feature Selection for On-line Signature Verification. In: International Conference on Document Analysis and Recognition (2005)
Muralidharan, N., Wunnava, S.: Signature Verification: A Popular Biometric technology. In: Second LACCEI International Latin American and Caribbean Conference for Engineering and Technology (2004)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, California (1999)
Li, T.S.: Feature Selection for Classification by using a GA-Based Neural Network Approach. Journal of the Chinese Institute of Industrial Engineers 23, 55–64 (2006)
Zamalloa, M., Bordel, G.: Feature Selection Based on Genetic Algorithm for Speaker Recognition. In: The Speaker and Language Recognition Workshop (2006)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley Publishing Company, Inc., Reading (1989)
Yang, X., Furuhashi, T.: A study on signature verification using a new approach to genetic based machine learning. In: IEEE International Conference on Systems, Man and Cybernetics 1995. Intelligent Systems for the 21st Century, vol. 5, pp. 4383–4386 (1995)
Yang, X., Furuhashi, T.: Constructing a High Performance Signature Verification System Using a GA Method. In: Second New Zeeland International Two-Stream Conference on Artificial Neural Networks and Expert Systems, pp. 170–173 (1995)
Wijesoma, W.S.: Selecting optimal personalized features for on-line signature verification using GA. In: IEEE International on Systems, Man, and Cyb., vol. 4, pp. 2740–2745 (2000)
Galbally, J., Fierrez, J.: Feature selection based on genetic algorithms for on-line signature verification. In: IEEE Workshop on Automatic Identification Advanced Technologies, AutoID, pp. 198-203 (2007)
Tsoukalas, L.H., Uhrig, R.E.: Fuzzy and Neural Approaches in Engineering. John Wiley and Sons, Inc., New York (1997)
Slyter, A.: Forensic Signature Examination. Thomas Publisher, U.S.A (1995)
ACE CAD Enterprise Co., http://www.acecad.com.tw
Smith, S.W.: Digital Signal Processing. In: Newness (ed.). Elsevier, Oxford (2003)
Oh, I.-S., Lee, J.-S.: Hybrid Genetic Algorithms for Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11) (2004)
Baluja, S.: An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics. School of Comp. Sc. Carnegie Mellon Uni. Pennsylvania (1995)
Pujol, O., Radeva, P., Vitria, J.: Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(6) (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Romo, J.C., Luna-Rosas, F.J., Mora-González, M. (2009). On-Line Signature Verification Based on Genetic Optimization and Neural-Network-Driven Fuzzy Reasoning. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-05258-3_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05257-6
Online ISBN: 978-3-642-05258-3
eBook Packages: Computer ScienceComputer Science (R0)