Study on Image Processing Based Sign Lock Verification System
Keywords:
Feature based techniques, Function based technique, thrusters based technique, colour based technique, SVM, HOGAbstract
The signature verification is the behavioral parameter of biometrics and is used to authenticate a person. A typical signature verification system generally consists of four components: data acquisition, pre-processing, feature extraction and verification. Very large percentage of daily financial transactions is generally carried out on the basis of verification of signatures. Therefore signature plays an important role both for authentication and authorization of any legal documents. Many documents, such as forms, contracts, bank checks, and credit card transactions require the signing of a signature. Therefore, it is of upmost importance to be able to recognize signatures accurately, effortlessly, and in a timely manner. In this paper we will describe the different methods by using which we can easily verify the signature of individual user in this paper, the use of One-Class Support Vector Machine (OC-SVM) based on writer-independent parameters, which takes into consideration only genuine signatures and when forgery signatures are lack as counter examples for designing the HSVS (Handwritten Signature Verification System).
References
- Bhanu Panjwani, Deval C. Mehta, “Hardware-software co-design of elliptic curve digital signature algorithm over binary fields,” IEEE Xplore, International Conference on Advances in Computing, Communications and Informatics, 2015
- Muhammad Sarfraz, and Syed M. A. J. Rizvi, “An Intelligent System for Online Signature Verification” proceedings of the IEEE, ISBN: 978-1-4673-6988-6, 2015
- L. R. Rabiner, A tutorial on hidden markov-models and selected applications in speech recognition, Proceedings of the IEEE, Vol.77, No.2, 1989, pp.257-286
- R. Plamondon and S.N. Srihari, “On-line and off-line handwriting recognition: A comprehensive survey,” IEEE Trans. PAMI, Vol. 22, No. 1, pp. 63–84, 2000.
- Maltoni D., Maio D., Jain A., Prabhakar S.: « Handbook of Fingerprint Recognition », Springer 2009.
- M. H. Lim and P. C. Yuen, “Entropy measurement for biometric verification systems,” IEEE Trans. Cybern., vol. 46, no. 5, pp. 1065–1077, May 2016
- M. A. Ferrer, M. Diaz-Cabrera, and A. Morales, “Static signature synthesis: A neuromotor inspired approach for biometrics,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 667–680, Mar. 2015.
- R. Plamondon, C. O’Reilly, J. Galbally, A. Almaksour, and E. Anquetil, “Recent developments in the study of rapid human movements with the kinematic theory: Applications to handwriting and signature synthesis,” Pattern Recognition. Letts, vol. 35, pp. 225–235, Jan. 2014.
- Bhattacharyya D., Bandyopadhyay S., Das P., Ganguly D., Mukherjee S., 2008, “Statistical Approach for Offline Handwritten Signature Verification”, Journal of Computer Science, 4 (3), pp. 181-185.
- Shams. I. Ben, 2007, “Signature Recognition by Segmentation and Regular Line Detection”, In Proceedings of Tencon-2007 IEEE Region 10 Conference, pp. 1-4.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET

This work is licensed under a Creative Commons Attribution 4.0 International License.