Abstract
Signature is a behavioral trait of an individual and forms a special class of handwriting in which legible letters or words may not be exhibited. Signature Verification System (SVS) can be classified as either offline or online. [1] In this paper, we used vector quantization technique for signature verification. The data is captured at a later time by using an optical scanner to convert the image into a bit pattern. The features thus extracted are said to be static. Our system is designed using cluster based features which are modeled using vector quantization as its density matching property provides improved results compared to statistical techniques. The classification ratio achieved using Vector Quantization is 67%.
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Jain, C., Singh, P., Chugh, A.: An Offline Signature Verification using Adaptive Resonance Theory 1 (ART1). International Journal of Computer Applications (0975 – 8887) 94(2), 8–11 (2014)
Jain, C., Singh, P., Chugh, A.: An optimal approach to offline signature verification using GMM. In: Proc. of the International Conference on Science and Engineering (ICSE 2011), pp. 102–106 (June 2011)
Jain, C., Singh, P., Chugh, A.: Performance Considerations in Implementing Offline Signature Verification System. International Journal of Computer Applications (0975 – 8887) 46(11) (May 2012)
Ferrer, M.A., Alonso, J.B., Travieso, C.M.: Offline Geometric Parameters for Automatic Signature Verification Using Fixed- Point Arithmetic. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 993–997 (2005)
Bowyer, K., Govindaraju, V., Ratha, N.: Introduction to the special issue on recent advances in biometric systems. IEEE Transactions on Systems, Man and Cybernetics 37(5), 1091–1095 (2007)
Zhang, D., Campbell, J., Maltoni, D., Bolle, R.: Special issue on biometric systems. IEEE Transactions on Systems, Manand Cybernetics 35(3), 273–275 (2005)
Prabhakar, S., Kittler, J., Maltoni, D., O’Gorman, L., Tan, T.: Introduction to the special issue on biometrics: progress and directions. Pattern Analysis and Machine Intelligence 29(4), 513–516 (2007)
Vargas, J.F., Ferrer, M.A., Travieso, C.M., Alonso, J.B.: Off-line signature verification based on grey level information using texture features. Pattern Recognition(Elsevier) 44, 375–385 (2011)
Hanmandlu, M., Yusof, M.H.M., Madasu, V.K.: Off-line signature verification and forgery detection using fuzzy modeling. Pattern Recognition (Elesevier) 38, 341–356 (2005)
Uppalapati, D.: Thesis on Integration of Offline and Online Signature Verification systems. Department of Computer Science and Engineering, I.I.T., Kanpur (July 2007)
Aykanat, C.: Offline Signature Recognition and Verification using Neural Network. In: Proceedings of the 19th International Symposium on Computer and Information Sciences, pp. 373–380. Springer, Heidelberg (2004)
Kisku, D.R., Gupta, P., Sing, J.K.: Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory. Proceeding of International Journal of Security and It’s Applications 4(3), 35–44 (2010)
Baltzakisa, H., Papamarkos, N.: A new signature verification technique based on a two-stage neural network classifier. Engineering Applications of Artificial Intelligence 14, 95–103 (2001)
Özgündüz, E., Şentürkand, T., Elif Karslıgil, M.: Offline Signature Verification And Recognition By Support Vector Machine, Antalya, Turkey, pp. 113–116 (September 2005)
Kalera, M.K.: Offline Signature Verification And Identification Using Distance Statistics. International Journal of Pattern Recognition and Artificial Intelligence 18(7), 1339–1360 (2004)
Karouni, A., Daya, B., Bahlak, S.: Offline signature recognition using neural networks approach. Procedia Computer Science 3, 155–161 (2011)
Ahmad, S.M.S., Shakil, A., Faudzi, M.A., Anwar, R.M., Balbed, M.A.M.: A Hybrid Statistical Modeling, Normalization and Inferencing Techniques of an Off-line Signature Verification System. In: World Congress on Computer Science and Information Engineering (2009)
Nguyen, V., Blumenstein, M., Leedham, G.: Global Features for the Off-Line Signature Verification Problem. In: IEEE International Conference on Document Analysis and Recognition (2009)
Bansal, A., Garg, D., Gupta, A.: A Pattern Matching Classifier for Offline Signature Verification. In: IEEE Computer Society First International Conference on Emerging Trends in Engineering and Technology (2008)
Kiani, V., Pourreza, R., Pourreza, H.R.: Offline Signature Verification Using Local Radon Transform and SVM. International Journal of Image Processing 3(5), 184–194 (2009)
Das, M.T., Dulger, L.C.: Signature verification (SV) toolbox: - Application of PSO-NN. Engineering Applications of Artificial Intelligence 22(4), 688–694 (2009)
Maya, V., Karki, K., Indira, S., Selvi, S.: Off-Line Signature Recognition and Verification using Neural Network. In: International Conference on Computational Intelligence and Multimedia Applications, pp. 307–312 (December 2007)
Shukla, N., Shandilya, M.: Invariant Features Comparison in Hidden Markov Model and SIFT for Offline Handwritten Signature Database. International Journal of Computer Applications 2(7), 975–995 (2010)
Wen, J., Fang, B., Tang, Y.Y., Zhang, T.P.: Model-based signature verification with rotation invariant features. Pattern Recognition 42(7), 1458–1466 (2009)
Fahmy, M.M.M.: Online handwritten signature verification system based on DWT features extraction and neural network classification. Ain Shams Engineering Journal 1(1), 59–70 (2010)
Batista, L., Granger, E., Sabourin, R.: Dynamic selection of generative–discriminative ensembles for off-line signature verification. Pattern Recognition 45(4), 1326–1340 (2012)
Radhika, K.R., Venkatesha, M.K., Sekhar, G.N.: Signature authentication based on subpattern analysis. Applied Soft Computing 11(3), 3218–3228 (2011)
Samuel, D., Samuel, I.: Novel feature extraction technique for off-line signature verification system. International Journal of Engineering Science and Technology 2(7), 3137–3143 (2010)
Deepa, S.N.: Introduction to Neural Network. Tata McGraw Hill (2006)
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Chugh, A., Jain, C., Singh, P., Rana, P. (2015). Learning Approach for Offline Signature Verification Using Vector Quantization Technique. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_38
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DOI: https://doi.org/10.1007/978-3-319-13728-5_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13727-8
Online ISBN: 978-3-319-13728-5
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