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Advanced Signature Verification Techniques: A Review

Shivam Sharma, Yash Jain, Yagya Bharadwaj, Deepak Moud

Abstract


There are various authentication techniques existing in these days to verify the originality of the owner’s identification, based on new technology and human computer interfaces like voice recognition and image processing like face detection methods to avoid the frauds. The popular noncomputer vision-based techniques like fingerprint authentication and passwords are most popular now, but what about the traditional method of the authenticity i.e., handwritten signature. In this era of technology and computer vision, the handwritten signatures are still the most popular way to represent a person’s identity and verification, it is like a “Seal of verification and authentication”. This research work is a study of advanced signature verification system as in this era it is very complicated to avoid vulnerabilities in such a system. This research work is a study of the signature verification system based on Artificial Neural Networks (ANN) that gave the confidence of a given signature if it is forged or not. In this study, a solution based on Convolutional Neural Networks (CNN) is represented which gives the result that whether the given signature is forged or original.


Keywords


Offline signature verification, FAR (False Acceptance Rate), FRR (False Rejection Rate), CNN (Convolutional Neural Network)

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References


J. Zhang, X. Zeng, Y. Lu1, L. Zhang, M. Li, “A Novel Off-line Signature Verification Based on One-class-one network” Third International Conference on Natural Computation, 2007, (ICNC-2007), pp. 590 – 594.

Great Learning. Sampriti Chatterjee (29-Oct-2021). What is Feature Extraction? Feature Extraction in Image Processing [Online]. Available from https://www.mygreatlearning.com/blog/feature-extraction-in-image-processing/

Collins. Forgeries [online]. Available from https://www.collinsdictionary.com/dictionary/english/forgeries

Tech Target. Ben Lutkevich. database (DB): What is a database? [Online]. Available from https://www.techtarget.com/searchdatamanagement/definition/database

Saba Mushtaq, A.H. Mir, “Signature Verification: A Study” 4th International Conference on Computer and Communication Technology (ICCCT),2013.

R. Plamondon and S.N. Srihari, "Online and Offline Handwriting Recognition: A Comprehensive Survey", IEEE Tran. on Pattern Analysis and Machine Intelligence, vol.22 no.1, pp.63-84, Jan.2000.

Ashwini Pansare, Shalini Bhatia, “Handwritten Signature Verification using Neural Network”, International Journal of Applied Information Systems (IJAIS) – ISSN: 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 1– No.2, January 2012 – www.ijais.org

Walid Hussein, Mostafa A. Salama and Osman Ibrahim. Image Processing Based Signature Verification Technique to Reduce Fraud in Financial Institutions. MATEC Web of Conferences. 2016;76(05004):1-5.

Moud, D., Sharma, P., Kushwaha, R.C. (2021). A Comparative Analysis on Offline Signature Verification System Using Deep Convolutional Neural Network. In: Goyal, D., Gupta, A.K., Piuri, V., Ganzha, M., Paprzycki, M. (eds) Proceedings of the Second International Conference on Information Management and Machine Intelligence. Lecture Notes in Networks and Systems, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-15-9689-6_66

Moud, D., Tuli, S., Rana, R.P. (2021). A Review on Offline Signature Verification Using Deep Convolution Neural Network. In: Goyal, D., Bălaş, V.E., Mukherjee, A., Hugo C. de Albuquerque, V., Gupta, A.K. (eds) Information Management and Machine Intelligence. ICIMMI 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-4936-6_2

Specin Forensics LLC, Handwriting and forgery Examination, hhtp://4n6.com/handwriting-and-forgery-examination/

Manoj Kumar, “Signature Verification Using Neural Network”, International Journal on Computer Science and Engineering (IJCSE) ISSN: 0975-3397, Vol. 4 No. 09 Sep 2012




DOI: https://doi.org/10.37591/jons.v10i1.898

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