Static signatures are the signatures in the offline documents. The genuineness of these signatures is very important to verify the originality of the documents. There are many methods available to check the authentication of the static signatures. In this paper, NASNetMobile and NASNetLarge deep learning architectures are proposed for static signature verification (SSV). These architectures were built with ImageNet weights and then fine-tuned to distinguish real and fake signatures. Initially, the architectures have been trained using signatures from the ICDAR signature dataset, both original and augmented. The transfer learning approach was used to train all layers of the architectures. These architectures were evaluated on signature datasets with a multitude of genuine signature samples using the NASNetMobile and NASNetLarge for the both datasets. This comparison shows that the proposed strategy improves accuracy in verifying static signatures. The results achieved using the transfer learning process were compared to those obtained using deep learning architectures like CNN-trained architectures.
REFERENCES
1.
P.
Chauhan
, S.
Chandra
, S.
Maheshkar
. “Static digital signature recognition and verification using neural networks
”, In 2016 1st India International Conference on Information Processing (IICIP)
, 1
–6
(2016
).2.
R.
Ghosh
. “A Recurrent Neural Network-based deep learning model for offline signature verification and recognition system
”, Expert Systems with Applications
, 168
:114249
, (2021
).3.
V.
Ruiz
, I.
Linares
, A.
Sanchez
, J. F.
Velez
. “Off-line handwritten signature verification using the compositional synthetic generation of signatures and Siamese Neural Networks
”, Neurocomputing
, 374
: 30
–41
, (2020
).4.
H.
Suwanwiwat
, A.
Das
, U.
Pal
, M.
Blumenstein
. “ICFHR 2018 competition on Thai student signatures and name components recognition and verification (tsncrv2018
)”, In 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)
, IEEE
, 500
–505
, (2018
).5.
L. G.
Hafemann
, R.
Sabourin
, L. S.
Oliveira
. “Offline handwritten signature verification - a literature review
”, In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)
, IEEE
, 1
–8
, (2017
).6.
A.
Foroozandeh
, A. A.
Hemmat
, H.
Rabbani
. “Offline Handwritten Signature Verification and Recognition Based on Deep Transfer Learning
”, In 2020 International Conference on Machine Vision and Image Processing (MVIP)
, IEEE
, 1
–7
(2020
).7.
L. G.
Hafemann
, L. S.
Oliveira
, R.
Sabourin
. “Fixed-sized representation learning from offline handwritten signatures of different sizes
”, International Journal on Document Analysis and Recognition (IJDAR)
, 21
(3
): 219
– 232
(2018
).8.
L.G.
Hafemann
, R.
Sabourin
, and L.S.
Oliveira
, “Learning features for offline handwritten signature verification using deep convolutional neural networks
”, Pattern Recognition
, 70
: 163
–176
(2017
).9.
Y.
Zheng
, W.
Ohyama
, B. K.
Iwana
and S.
Uchida
, “Capturing micro deformations from pooling layers for offline signature verification
”, In 2019 International Conference on Document Analysis and Recognition (ICDAR)
, IEEE
, 1111
–1116
(2019
).10.
ZJ.
Xing
, F.
Yin
, YC.
Wu
, CL.
Liu
. “Offline signature verification using convolution Siamese network, International Society for Optics and Photonics
”, ninth International Conference on Graphic and Image Processing (ICGIP 2017)
, 10615
(2018
).11.
S.
Dey
, A.
Dutta
, J. I.
Toledo
, S. K.
Ghosh
, J.
Lladós
, and U.
Pal
, “Signet: Convolutional Siamese network for writer independent offline signature verification”
, 1707
–2131
(2017
).12.
A. B.
Jagtap
, D. D.
Sawat
, R. S.
Hegadi
, and R. S.
Hegadi
, “Verification of genuine and forged offline signatures using Siamese Neural Network (SNN
)”, Multimedia Tools and Applications
, 79
(47
): 35109
–35123
(2020
).13.
L. G.
Hafemann
, R.
Sabourin
, L. S.
Oliveira
. “Writer-independent features learning for offline signature verification using deep convolutional neural networks
” In 2016 international joint conference on neural networks (IJCNN)
, IEEE
, 2576
–2583
(2016
).14.
A.
Jain
, S. K.
Singh
, and K. P.
Singh
, “Handwritten signature verification using shallow convolutional neural network
”, Multimedia Tools and Applications
, 79
(27
): 19993
–20018
(2020
).15.
H.
Hezil
, R.
Djemili
, and H.
Bourouba
, “Signature recognition using binary features and KNN
”, International Journal of Biometrics
, 10
(1
): 1
–15
(2018
).16.
Y.
Serdouk
, H. Nemmour
and
, Y.
Chibani
, “New off-line handwritten signature verification method based on artificial immune recognition system
”, Expert Systems with Applications
, 51
:186
–194
(2016
).17.
E. N.
Zois
, L.
Alewijnse
, and G.
Economou
, “Offline signature verification and quality characterization using poset-oriented grid features
”, Pattern Recognition
, 54
: 162
–177
(2016
).18.
E. N.
Zois
, E.
Zervas
, D.
Tsourounis
, G.
Economou
, “Sequential motif profiles and topological plots for offline signature verification
”, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
, 13248
–13258
(2020
).19.
P.
Maergner
, V.
Pondenkandath
, M.
Alberti
, M.
Liwicki
, K.
Riesen
, R.
Ingold
, and A.
Fischer
, “Combining graph edit distance and triplet networks for offline signature verification
”, Pattern Recognition Letters
, 125
: 527
–533
(2019
).20.
L.
Liu
, F.
Huang
, Y.
Yin
, Chen
. “Off-line signature verification using a region-based metric learning network
”, In Chinese Conference on Pattern Recognition and Computer Vision (PRCV)
, 74
–86
(2018
).21.
D.
Banerjee
, B.
Chatterjee
, P.
Bhowal
, T.
Bhattacharyya
, S.
Malakar
, and R.
Sarkar
, “A new wrapper feature selection method for language-invariant offline signature verification
”, Expert Systems with Applications
, 186
: 115756
(2021
).22.
Y.
Zheng
, B. K.
Iwana
, M. I.
Malik
, S.
Ahmed
, W.
Ohyama
, and S.
Uchida
, “Learning the micro deformations by max-pooling for offline signature verification
”, Pattern Recognition
, 118
: 108008
(2021
).23.
Q.
Wan
, Q.
Zou
, “Learning Metric Features for Writer-Independent Signature Verification using Dual Triplet Loss
”, In 2020 25th International Conference on Pattern Recognition (ICPR)
, IEEE
, 3853
–3859
(2021
).24.
K.
Vohra
, “Signature Verification Using Support Vector Machine and Convolution Neural Network
”, Turkish Journal of Computer and Mathematics Education (TURCOMAT)
, 12
(1S
), 80
–89
(2021
).25.
B.
Zoph
, V.
Vasudevan
, J.
Shlens
, QV
Le
. “Learning transferable architectures for scalable image recognition
”, In Proceedings of the IEEE conference on computer vision and pattern recognition
, 8697
–8710
(2018
).26.
M.
Hussain
, J. J.
Bird
, and D. R.
Faria
, “A study on CNN transfer learning for image classification
”, In UK Workshop on Computational Intelligence
, 191
–202
(2018
).28.
U.
Atila
, M.
Uçar
, K.
Akyol
, and E.
Uçar
, “Plant leaf disease classification using Efficient Net deep learning model
”, Ecological Informatics
, 61
:101182
(2021
).30.
M.
Li
, T.
Zhang
, Y.
Chen
, A. J.
Smola
, “Efficient mini-batch training for stochastic optimization
”, In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
, 661
–670
(2014
).31.
K.S.
Sharvari
, “A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection
”, 23
(7
):1129
–1139
(2021
).32.
H. H.
Kao
, C. Y.
Wen
, “Offline signature verification and forgery detection method based on a single known sample and an explainable deep learning approach
”, Applied Sciences
, 10
(11
):3716
(2020
).33.
S. M.
Navid
, S. H.
Priya
, N. H.
Khandakar
, Z.
Ferdous
, A. B.
Haque
, “Signature Verification Using Convolutional Neural Network
”, In 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)
, 35
–39
(2019
).
This content is only available via PDF.
©2023 Authors. Published by AIP Publishing.
2023
Author(s)
You do not currently have access to this content.
