Verification of genuine and forged offline signatures using Siamese Neural Network (SNN)

Jagtap, Amruta B.; Sawat, Dattatray D.; Hegadi, Rajendra; Hegadi, Ravindra S.

doi:10.1007/s11042-020-08857-y

Cited by 40 publications

(21 citation statements)

References 25 publications

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“…The authors mentioned that, their system works well within large number of signed documents. Amruta B. et al, [14], represent siamese neural network (SNN) using CNN as subnetwork for signature verification. The network architecture shares same weights among number of sub networks inside it.…”

Section: Related Workmentioning

confidence: 99%

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An accurate signature verification system based on proposed HSC approach and ANN architecture

Kadhm

Mohammed

Ayad

2021

IJEECS

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<span>With the rapid development of technology in all life fields, and due to the huge daily needs for banking systems process, documents processing and other similar systems. The authentication became more required key for these systems. One of the successful system to verify the any person is the signature verification system. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. Therefore, this paper present a reliable signature verification system using proposed histogram of sparse codes (HSC) feature extraction approach and artificial neural networks (ANN) architecture for classification. The system achieved fast computing 0.09 ms and accurate verification results that is 99.7% using three different signature images datasets CEDAR, UTSig, and ICDAR.</span>

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Section: Related Workmentioning

confidence: 99%

“…Table 4. Comparison results of proposed system and other systems Method Accuracy (%) sCNN [11] 93 CNN [12] 90 -94 SNN [14] 88.49 SVM [15] 89.58 ANN [27] 89.24 Proposed 99.7…”

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confidence: 99%

An accurate signature verification system based on proposed HSC approach and ANN architecture

Kadhm

Mohammed

Ayad

2021

IJEECS

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“…Finally, the difference (similarity-difference) of the features extracted by each of the twin networks is found by calculating the distance metric with the loss function applied in the last output layer. The contrastive loss, which is a loss function mostly used in Siamese networks, is defined in (1) [35]. Since the Siamese architecture yields successful results in the scenarios of comparing the similarities of the picture images, this architecture has been specially preferred.…”

Section: Cnn (Convolutional Neural Network Cnn) and Siamese Networkmentioning

confidence: 99%

“…f is the embedding function that matches a signature image with a real vector space through CNN. w1, w2 are the learned weights for a particular layer of the network [35].…”

Section: Cnn (Convolutional Neural Network Cnn) and Siamese Networkmentioning

confidence: 99%

Siamese Sinir Ağı One-Shot Öğrenmeyi Kullanarak İmza Doğrulama

Arısoy

2021

International Journal of Engineering and Innovative Research

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With the acceleration of digitalization in all areas of our lives, the need for biometric verification methods is increasing. The fact that biometric data is unique and biometric verification is stronger against phishing attacks compared to password-based authentication methods, has increased its preference rate. Signature verification, which is one of the biometric verification types, plays an important role in many areas such as banking systems, administrative and judicial applications. There are 2 types of signature verification, online and offline, for identifying the identity of the person and detecting signature forgery. Online signature verification is carried out during signing and temporal dynamic data are available regarding the person's signature. Offline verification is applied by scanning the image after signing, and this verification is limited to spatial data. Therefore, the offline signature verification process is considered a more challenging task.In this study, offline signature verification, independent of the writer, based on One-Shot Learning, was performed using Siamese Neural Network. Due to the fact that the Deep Convolution Neural Network requires a large amount of labeled data for image classification, real and fake signature distinction has been achieved by using the One-Shot Learning method, which can perform a successful classification by using less numbers of signature images. As a result of the experiments conducted on signature datasets, using the Siamese architecture, the proposed approach achieved percentage accuracy of 93.23, 90.11, 89.99, 92.35 verification in 4NSigComp2012, SigComp2011, 4NSigComp2010 and BHsig260 respectively.

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“…In the papers [13], [20] the authors have developed an offline writer-independent signature verification system using convolutional siamese network, by calculating the euclidean distance between the pairs. The authors of [14], [18] have made use of the Convolutional Neural Network model, and ReLu activation function to classify signature as original or forged one.…”

Section: Related Workmentioning

confidence: 99%

Offline Handwritten Text Recognition and Signature Verification

Arai¹,

A²,

Jain³

et al. 2021

Preprint

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Offline Handwritten Text Recognition (HTR) is one of the most interesting challenges in today's date in the field of Image processing. This paper introduces a novel technique to recognize the handwritten text by using Convolutional Recurrent Neural Network along with Connectionist Temporal Classification. This model makes use of the IAM dataset. Offline Signature Verification (SV) is another challenging task in the field of biometrics. This paper demonstrates a novel technique to verify the signature as an original or forged one, and makes use of the Convolutional Siamese network.

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Verification of genuine and forged offline signatures using Siamese Neural Network (SNN)

Cited by 40 publications

References 25 publications

An accurate signature verification system based on proposed HSC approach and ANN architecture

An accurate signature verification system based on proposed HSC approach and ANN architecture

Siamese Sinir Ağı One-Shot Öğrenmeyi Kullanarak İmza Doğrulama

Offline Handwritten Text Recognition and Signature Verification

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