Offline Signature Recognition using Radon Transform

Authors: M.Radmehr, S.M.Anisheh, I.Yousefian

Abstract:

In this work a new offline signature recognition system based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained vectors are calculated to construct a feature vector for each signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of the system several experiments are carried out. Offline signature database from signature verification competition (SVC) 2004 is used during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition.

Keywords: Fractal Dimension, Offline Signature Recognition, Radon Transform, Support Vector Machine

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063066

References:

[1] O. Faundez-Zanuy, M., Biometric recognition: why not massively adopted Syst. Mag. 20 (8) (2005) 25-28.
[2] Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W., Guide to Biometrics. Springer, New York, (2004).
[3] D.Y. Yeung, et al., SVC2004: first international signature verification competition, in: International Conference on Biometric Authentication 2004, Fort Lauderdale, USA, July 15-17, (2004), 16-22.
[4] Frias-Martinez, E., Sanchez, A., Velez, J., Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition, Engineering Applications of Artificial Intelligence 19 (2006), 693-704.
[5] Ammar, M., Yoshida, Y., Fukumura, T., Structural description and classification of signature images. Pattern Recognition 23, (1990), 697-710.
[6] Han, K., Sethi, K., Handwritten signature identification. Pattern Recognition Letters 17, (1996), 83-90.
[7] Pavlidis, I., Papanikolopoulos, N.P., Mavuduru, R., Signature Identification through the use of deformable structures. Signal Processing 71, (1998), 187-201.
[8] Riba, J.R., et al., Method for invariant signature classification. Proceedings of 15th International Conference on Pattern Recognition 2, (2000), 953-956.
[9] Frias-Martinez, E., Sanchez, A., Velez, J., Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition, Engineering Applications of Artificial Intelligence, 19, (2006), 693-704.
[10] ASANO, A. "RADON TRANSFORMATION AND PROJECTION THEOREM", TOPIC 5, LECTURE NOTES OF SUBJECT PATTERN INFORMATION PROCESSING, 2002 AUTUMN SEMESTER.
[11] Kupce, E., Freeman, R., "The Radon Transform: A New Scheme for Fast Multidimensional NMR", Concepts in Magnetic Resonance, Wiley Periodicals, Vol. 22, pp. 4-11, 2004.
[12] Falconer, J., Fractal Geometry-Mathematical Foundations and Applications, John Wiley and Sons, 2003.
[13] Tykierko, M., Using invariants to change detection in dynamical system with chaos, Physica D: Nonlinear Phenomena, vol. 237, pp. 6-13, 2008.
[14] Higuchi, T., Aproach to an irregular time series on the basis of the fractal theory, Physica D, vol. 31, pp. 277-283, 1988.
[15] Petrosian, A., Kolmogorov Complexity of Finite Sequences and Recognition of Different Preictal EEG Patterns, Proc. IEEE Symposium on Computer-Based Medical Systems, vol. 5, pp. 212-217, 1995.
[16] Katz, M. J., Fractals and the Analysis of Waveforms, Comput. Biol. Med., vol. 18, no. 3, pp. 145-156, 1988.
[17] Esteller, R., Vachtsevanos, G., Echauz, J., and litt, B., A comparison of fractal dimension algorithms using synthetic and experimental data, IEEE Trans. Circuits Syst., vol. 48, PP. 177-183, 2001.
[18] Boser, B. E., Guyon, I. M., Vapnik, V. N., 1992. A training algorithm for optimal margin classifiers. Proceedings of Fifth Annual Workshop on Computational Learning Theory. pp. 144-152.
[19] Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge.
[20] Frias-Martinez, E., Sanchez, A., Velez, J., Support vector machines versus multi layer perceptrons for efficient off-line signature recognition, Engineering Applications of Artificial Intelligence 19 (2006) 693-704.
[21] http://www.cse.ust.hk/svc2004/download.html.
[22] Lv, H., Wang, W., Wang, C., Zhuo, Q., Off-line Chinese signature verification based on support vector machines, Pattern Recognition Letters 26 (2005) 2390-2399.