Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition

Engineering Applications of Artificial Intelligence

Volumn 19, Issue 6, 2006, Pages 693-704

  Frias Martinez, E ^a   Sanchez, A ^a   Velez, J ^a  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

Author keywords

Multi layer perceptrons; Off line signature recognition; Support vector machines

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMATION; FEATURE EXTRACTION; LEARNING SYSTEMS; NEURAL NETWORKS; SECURITY OF DATA;

FEATURE VECTOR; MULTI-LAYER PERCEPTRONS (MLP); OFF-LINE SIGNATURE RECOGNITION; SUPPORT VECTOR MACHINES (SVM);

ELECTRONIC DOCUMENT IDENTIFICATION SYSTEMS;

EID: 33747881516     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2005.12.006     Document Type: Article

References (45)

1. Abbas, R., 1994. A prototype system for off-line signature verification using multilayered feedforward neural networks. Master Thesis, Royal Melbourne Institute of Technology (RMIT), Department of Computer Science, Melbourne, Australia.
2. Ammar M., Yoshida Y., and Fukumura T. Structural description and classification of signature images. Pattern Recognition 23 (1990) 697-710
3. Bahlmann, C., Haasdonk, B., Burkhardt, 2002. On-line handwriting recognition using support vector machines-a kernel approach. Proceedings of International Workshop on Frontiers in Handwriting Recognition (IWFHR). pp. 49-54.
4. Bajaj R., and Chaudhury S. Signature verification using multiple neural classifiers. Pattern Recognition 30 (1997) 1-7
5. Baltzakis H., and Papamarkos N. A new signature verification technique based on a two-stage neural network classifier. Engineering Applications of Artificial Intelligence 14 (2001) 95-103
6. Bellili, A., Giloux, M., Gallinari, P., 2001. An hybrid MLP-SVM handwritten digit recognizer. Proceedings of Sixth International Conference on Document Analysis and Recognition (ICDAR'01). pp. 28-33.
7. Bolle R.M., Connell J.H., Pankanti S., Ratha N.K., and Senior A.W. Guide to Biometrics (2004), Springer, New York
8. 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.
9. Bynm H. A survey on pattern recognition applications of Support Vector Machines. International Journal of Pattern Recognition and Artificial Intelligence 17 (2003) 459-486
10. Camino, J.L., Travieso, M.C., Morales, C.R., Ferrer, M.A., 1999. Signature classification by hidden Markov model. Proceedings of IEEE International Carnahan Conference on Security Technology. pp. 481-484.
11. Collobert R., and Bengio S. SVMTorch: support vector machines for large-scale regression problems. Journal of Machine Learning Research 1 (2002) 143-160
12. Collobert, R., Bengio, S., Mariéthoz, J., 2001. Torch: a modular machine learning software library. Technical Report IDIAP-RR 02-46, IDIAP.
13. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines (2000), Cambridge University Press, Cambridge
14. Fahlman S.E. Faster-learning variations on back-propagation: An empirical study. In: Sejnowski T.J., Hinton G.E., and Touretzky D.S. (Eds). Proceedings of 1988 Connectionist Models Summer School (1988), Morgan Kaufmann, San Mateo, CA
15. Fang, B., Wang, Y.Y., Leung, C.H., Tang, Y.Y., Kwok, P.C.K., Tse, K.W., Wong, Y.K., 1999. A smoothness index based approach for off-line signature verification. Proceedings of ICDAR'99, pp. 785-787.
16. Gorgevik, D., Cakmakov, D., Radevski, V., 2001. Handwritten digit recognition by combining support vector machines using rule-based reasoning. In: Proceedings of the 23rd International Conference Information Technology Interfaces (ITI). pp. 139-144.
17. Guo, G., Li, S. Z., Chan, K., 2000. Face recognition by support vector machines. In: Proceedings of Fourth IEEE International Conference on Face and Gesture Recognition. pp. 196-201.
18. Han, K., Sethi, K., 1995. Signature identification via local association of features. Proceedings of Third International. Conference on Document Analysis and Recognition, Montreal, Canada. pp. 187-190.
19. Han K., and Sethi K. Handwritten signature identification. Pattern Recognition Letters 17 (1996) 83-90
20. Hobby J.D. Using shape and layout information to find signatures, text, and graphics. Computer Vision and Image Understanding, 80 (2000) 88-110
21. Ismail M.A., and Gad S. Off-line Arabic signature recognition and verification. Pattern Recognition 33 (2000) 1727-1740
22. Java Neural Network Simulator, 2003. http://www-ra.informatik.uni-tuebingen.de/software/JavaNNS/welcome.html.
23. Justino, E.J.R., Bortolozzi, F., Sabourin, R., 2003. An off-line signature verification method based on SVM classifier and graphometric features. Internationa. Conference on Advances in Pattern Recognition (ICAPR 2003), Calcutta, India. pp. 10-13.
24. Kholmatov, A.A., 2003. Biometric identity verification using on-line and off-line signature verification. M.Sc. Thesis, Sabanci University, Istanbul, Turkey.
25. Kim S.H. Recognition of handwritten Korean address strings by tight-coupling of minimum distance classification and dictionary-based post-processing. Computer Processing of Oriental Languages 12 (1998) 207-221
26. Le Cun, Y., Jackel, L.D., Bottou, L., Brunot, A., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Muller, U.A., Sackinger, E., Simard, P., Vapnick, V., 1995. Comparisson of learning algorithms for handwritten digit recognition. Proceedings of International Conference on Artificial Neural Networks (ICANN'95). pp. 53-60.
27. Leclerc F., and Plamondon R. Automatic signature verification: the state of the art. International Journal on Pattern Recognition and Artificial Intelligence 8 (1994) 643-660
28. Lee S., and Pan J.C. Offline tracing and representation of signatures. IEEE Transactions. Systems, Man, and Cybernetics 22 (1992) 755-771
29. Lippmann R.P. An introduction to computing with neural nets. IEEE ASSP Magazine (1987) 4-28
30. Looney C.G. Pattern Recognition using Neural Networks: Theory and Algorithms for Engineers and Scientists (1997), Oxford University Press, Oxford
31. Madhvanath S., and Govindaraju V. The role of holistic paradigms in handwritten word recognition. IEEE Transactions. Pattern Analysis and Machine Intelligence 23 (2001) 149-164
32. Pavlidis, I., Mavuduru, R., Papanikolopoulos, 1994. Off-line recognition of signatures using revolving active deformable models. Proceedings of IEEE International Conference on Systems, Man and Cybernetics. pp. 771-776.
33. Pavlidis I., Papanikolopoulos N.P., and Mavuduru R. Signature Identification through the use of deformable structures. Signal Processing 71 (1998) 187-201
34. Perez, A., Sanchez, A., Velez, J.F., 2004. Simplified stroke-based approach for off-line signature recognition. Proceedings of Second COST 275 Workshop-Biometrics on the Internet. pp. 89-94.
35. Plamondon R., and Lorette G. Automatic signature verification and writer identification: the state of the art. Pattern Recognition 22 (1989) 107-131
36. Plamondon R., and Shirari S.N. On-line and off-line handwriting recognition: a comprehensive survey. IEEE Transactions. Pattern Analysis and Machine Intelligence 22 (2000) 63-84
37. Pontil M., and Verri A. Support vector machines for 3-D object recognition. IEEE Transactions. Pattern Analysis and Machine Intelligence 20 (1998) 637-646
38. Ramesh V.E., and Murty M.N. Off-line signature verification using genetically optimized weighted features. Pattern Recognition 32 (1999) 217-233
39. Riba J.R., et al. Method for invariant signature classification. Proceedings of 15th International Conference on Pattern Recognition 2 (2000) 953-956
40. Samanta B., Al-Balushi K.R., and Al-Araimi S.A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 16 (2003) 657-665
41. Scholkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V., 1996. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. AI Memo No. 1599, MIT.
42. Sethi K.I., and Han K. Use of local structural association for retrieval and recognition signature images. Proceedings of the SPIE 2490 (1995) 125-136
43. SVC, 2004. First International Signature Verification Competition. http://www.cs.ust.hk/svc2004/download.html.
44. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer, New York
45. Velez, J.F., Sanchez, A., Moreno, A.B., 2003. Robust off-line signature verification using compression networks and positional cuttings. Proceedings of IEEE International Conference on Neural Networks for Signal Processing (NNSP '03). pp. 627-636.