State of Art Survey Signature Verification Techniques 2019
Currently a lot of time is needed for the verification of signature manually. The need of developing an automated checking system is felt because of signature forgery in various transactions. The dynamic signature is a biometric trait which is used in identification. The aim of the model is identifying correct signature for reducing fraudulent transactions. The idea is to duplicate a given signature a number of times and train the verifier with each of the resulting signatures. Automatic signature verification is an application of image processing. Signature Verification can be 1) Online (Dynamic) 2) Offline (Static). For duplication of signature one approach is using the Cognitive Inspired Model. The approach for creating human like signatures can be done by introducing Intra-Component and Inter-Component variability. This system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens. For classification classifiers like SVM, CNN can be used.
Miguel A. Ferrer, and Réjean Plamondon, Dynamic Signature Verification System Based on One Real Signature”, 2168-2267 _c 2016 IEEE
2)Zhihua Xia, Tuanjia Shi , Neal N. Xiong, Xingmuing Sun, Byeungwoo Jeun, “A Privacy-preserving handwritten signature verification method using combinational features and secure KNN”
3)Miguel A. Ferrer, Moises Diaz, Cristina Carmona-Duarte and Aythami Morales “A Behavioral Handwriting Model for Static and Dynamic Signature Synthesis” 0162-8828 (c) 2016 IEEE.
4)Andreas Fischer, Member, IEEE, and R´ejean Plamondon, Fellow, IEEE “Signature Veriﬁcation Based on the Kinematic Theory of Rapid Human Movements” 2168-2291 © 2016 IEEE.
5)Moises Diaz, Andreas Fischertt, Rejean Plamondon and Miguel A. Ferrer “Towards an Automatic On-Line Signature Verifier Using Only One Reference Per Signer” 2015 13th International Conference on Document Analysis and Recognition (ICDAR).
6)Biswajit Kar, Anirban Mukherjee , and Pranab K. Dutta “Stroke Point Wrapping-Based Reference Selection and Verification of Online Signature” 0018-9456 © 2017 IEEE.
7)Anastasia Beresneva, Anna Epishkina, Darina Shingalova “Handwritten Signature Attributes for its Verification” 978-1-5386-4340-2/18/$31.00©2018 IEEE .
8)Moises Diaz-Cabrera, Miguel A. Ferrer, Aythami Morales “Cognitive Inspired Model to Generate Duplicated Static Signature Images” 2167-6445/14 $31.00 © 2014 IEEE.
9)Moises Diaz, Miguel A. Ferrer, Eskander, Robert Saburin “Generation of Duplicated Offline Signature Images for Verification Systems” 0162-8828© 2016 IEEE.
10)Kamlesh Kumariand, V.K.Shrivastava “Factors Affecting The Accuracy of Automatic Sugnature Verification” 978-9-3805-4421-2/16/$31.00 ©2016IEEE
11)Syed Khaleel Ahmed, Member IEEE; Agileswari K. Ramasamy; Anis Salwa Mohd. Khairuddin and Jamaludin Omar, Member IEEE “Automatic Online Signature Veriﬁcation: A Prototype Using Neural Networks” 978–1–4244–4547–9/09/$26.00© 2009 IEEE.
12)Bhushan S. Thakare, Dr. Hemant R. Deshmukh “A Novel End-To-End Approach For Offline Signature Verification System” 978-1-5386-4273-3/18/$31.00 ©2018 IEEE
13)Mohsen Fayyaz, Mohammad HajizadehSaffar, MohammadSabokrou, M. Hoseini and M. Fathy “Online Signature Verification Based on Feature Representation” 978-4799-8818-1/15/$31.00 ©2015 IEEE.
14)Napa Sae-Bae and NasirMemon, Fellow, IEEE “Online Signature Veriﬁcation on Mobile Devices” 1556-6013 © 2014 IEEE.
15)Bhushan S. Thakare, Dr. Hemant R. Deshmukh “A combined feature extraction model using SIFT and LBP for offline signature verification system” 978-1-5386-4273-3/18/$31.00 ©2018 IEEE
16)Luiz G. Hafemann, Robert Sabourin, and Luiz S. Oliveira “Characterising and Evaluating adversarialexamoles for Offline Handwritten Signature Verification”.
17)Taraggy M. Ghanim,Ayman M. Nabil “Offline Signature Verification and Forgery Detection Approach” 978-1-5386-5111-7/18/$31.00 ©2018 IEEE.
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