Offline Signature Recognition using Radon Transform
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:55601", author = "M.Radmehr and S.M.Anisheh and I.Yousefian", title = "Offline Signature Recognition using Radon Transform", 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", volume = "6", number = "2", pages = "213-5", }
