Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification

One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures...
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Autor*in:	Neha Sharma [verfasserIn] Sheifali Gupta [verfasserIn] Heba G. Mohamed [verfasserIn] Divya Anand [verfasserIn] Juan Luis Vidal Mazón [verfasserIn] Deepali Gupta [verfasserIn] Nitin Goyal [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	signature verification two-channel Siamese network convolutional neural network deep learning

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 14(2022), 18, p 11484
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:18, p 11484

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su141811484

Katalog-ID:	DOAJ084790385

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spelling	10.3390/su141811484 doi (DE-627)DOAJ084790385 (DE-599)DOAJa73d281ada1d457aa5e211e948fa609d DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Neha Sharma verfasserin aut Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries. signature verification two-channel Siamese network convolutional neural network deep learning Environmental effects of industries and plants Renewable energy sources Environmental sciences Sheifali Gupta verfasserin aut Heba G. Mohamed verfasserin aut Divya Anand verfasserin aut Juan Luis Vidal Mazón verfasserin aut Deepali Gupta verfasserin aut Nitin Goyal verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 18, p 11484 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:18, p 11484 https://doi.org/10.3390/su141811484 kostenfrei https://doaj.org/article/a73d281ada1d457aa5e211e948fa609d kostenfrei https://www.mdpi.com/2071-1050/14/18/11484 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 18, p 11484
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allfieldsGer	10.3390/su141811484 doi (DE-627)DOAJ084790385 (DE-599)DOAJa73d281ada1d457aa5e211e948fa609d DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Neha Sharma verfasserin aut Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries. signature verification two-channel Siamese network convolutional neural network deep learning Environmental effects of industries and plants Renewable energy sources Environmental sciences Sheifali Gupta verfasserin aut Heba G. Mohamed verfasserin aut Divya Anand verfasserin aut Juan Luis Vidal Mazón verfasserin aut Deepali Gupta verfasserin aut Nitin Goyal verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 18, p 11484 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:18, p 11484 https://doi.org/10.3390/su141811484 kostenfrei https://doaj.org/article/a73d281ada1d457aa5e211e948fa609d kostenfrei https://www.mdpi.com/2071-1050/14/18/11484 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 18, p 11484
allfieldsSound	10.3390/su141811484 doi (DE-627)DOAJ084790385 (DE-599)DOAJa73d281ada1d457aa5e211e948fa609d DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Neha Sharma verfasserin aut Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries. signature verification two-channel Siamese network convolutional neural network deep learning Environmental effects of industries and plants Renewable energy sources Environmental sciences Sheifali Gupta verfasserin aut Heba G. Mohamed verfasserin aut Divya Anand verfasserin aut Juan Luis Vidal Mazón verfasserin aut Deepali Gupta verfasserin aut Nitin Goyal verfasserin aut In Sustainability MDPI AG, 2009 14(2022), 18, p 11484 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:14 year:2022 number:18, p 11484 https://doi.org/10.3390/su141811484 kostenfrei https://doaj.org/article/a73d281ada1d457aa5e211e948fa609d kostenfrei https://www.mdpi.com/2071-1050/14/18/11484 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 18, p 11484
language	English
source	In Sustainability 14(2022), 18, p 11484 volume:14 year:2022 number:18, p 11484
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topic_facet	signature verification two-channel Siamese network convolutional neural network deep learning Environmental effects of industries and plants Renewable energy sources Environmental sciences
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container_title	Sustainability
authorswithroles_txt_mv	Neha Sharma @@aut@@ Sheifali Gupta @@aut@@ Heba G. Mohamed @@aut@@ Divya Anand @@aut@@ Juan Luis Vidal Mazón @@aut@@ Deepali Gupta @@aut@@ Nitin Goyal @@aut@@
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callnumber-first	T - Technology
author	Neha Sharma
spellingShingle	Neha Sharma misc TD194-195 misc TJ807-830 misc GE1-350 misc signature verification misc two-channel misc Siamese network misc convolutional neural network misc deep learning misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification
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topic_title	TD194-195 TJ807-830 GE1-350 Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification signature verification two-channel Siamese network convolutional neural network deep learning
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc signature verification misc two-channel misc Siamese network misc convolutional neural network misc deep learning misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc signature verification misc two-channel misc Siamese network misc convolutional neural network misc deep learning misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification
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title_full	Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification
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author_browse	Neha Sharma Sheifali Gupta Heba G. Mohamed Divya Anand Juan Luis Vidal Mazón Deepali Gupta Nitin Goyal
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title_sort	siamese convolutional neural network-based twin structure model for independent offline signature verification
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title_auth	Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification
abstract	One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries.
abstractGer	One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries.
abstract_unstemmed	One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries.
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title_short	Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification
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author2	Sheifali Gupta Heba G. Mohamed Divya Anand Juan Luis Vidal Mazón Deepali Gupta Nitin Goyal
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Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification

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