Offline Chinese Signature Verification Based on AlexNet

Cui, Wencheng; Guo, Xiaopeng; Shao, Hong; Zou, Limin

doi:10.1007/978-3-319-73317-3_5

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…The loss function is used to evaluate the predicted value and the real value of the model. When training the model in deep learning, the loss function is calculated and the model parameters are updated, thereby reducing the optimization error, until the loss function drops to the target value or reaches the training times [21]. This paper generates three pairs of merged features for the model by inverting the gray value of the signature image: reference signature and test signature, inverse grayscale reference signature and test signature, and reference signature and inverse grayscale test signature, as shown in Figure 4.…”

Section: Loss Functionmentioning

confidence: 99%

Multilingual Offline Signature Verification Based on Improved Inverse Discriminator Network

et al. 2022

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To further improve the accuracy of multilingual off-line handwritten signature verification, this paper studies the off-line handwritten signature verification of monolingual and multilingual mixture and proposes an improved verification network (IDN), which adopts user-independent (WI) handwritten signature verification, to determine the true signature or false signature. The IDN model contains four neural network streams with shared weights, of which two receiving the original signature images are the discriminative streams, and the other two streams are the reverse stream of the gray inversion image. The enhanced spatial attention models connect the discriminative streams and reverse flow to realize message propagation. The IDN model uses the channel attention mechanism (SE) and the improved spatial attention module (ESA) to propose the effective feature information of signature verification. Since there is no suitable multilingual signature data set, this paper collects two language data sets (Chinese and Uyghur), including 100,000 signatures of 200 people. Our method is tested on the self-built data set and the public data sets of Bengali (BHsig-B) and Hindi (BHsig-H). The method proposed in this paper has the highest discrimination rate of FRR of 10.5%, FAR of 2.06%, and ACC of 96.33% for the mixture of two languages.

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Section: Loss Functionmentioning

confidence: 99%

Multilingual Offline Signature Verification Based on Improved Inverse Discriminator Network

et al. 2022

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“…In [25] they applied the AlexNet, which is a convolutional neural network algorithm, so as to recognize the offline Chinese signature. Depending on the writer, they managed to distinguish between real and fake signatures.…”

Section: Related Workmentioning

confidence: 99%

Signature Verification Using Siamese Neural Network One-Shot Learning

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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“…A signature is a symbol of the name as it is commonly used, including an initial, stamp, signature stamp, or other signs as the representative of a signature or an electronic signature as intended in the information and electronic transactions. Signatures are a type of biometric authentication used to verify a person's identity [1]. Since ancient times, signature has been the government's most widely used and accepted individual biometric mark [2].…”

Section: Introductionmentioning

confidence: 99%

CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification

Fatihia

Fariza

Karlita

2023

JOIV : Int. J. Inform. Visualization

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Signature genuine verifications of offline hand-written signatures are critical for preventing forgery and fraud. With the growth of protecting personal identity and preventing fraud, the demand for an automatic system for signature verification is high. The signature verification system is then studied by many researchers using various methods, especially deep learning-based methods. Hence, deep learning has a problem. Deep learning requires much training time for the data to obtain the best model accuracy result. Therefore, this paper proposed a CNN Batch Normalization, the CNN architectural adaptation model with a normalization batch number added, to obtain a CNN model optimization with high accuracy and less training time for offline hand-written signature verification. We compare CNN with our proposed model in the experiments. The research method in this study is data collection, pre-processing, and testing using our private signature dataset (collected by capturing signature images using a smartphone), which becomes the difficulties of our study because of the different lighting, media, and pen used to sign. Experiment results show that our model ranks first, with a training accuracy of 88.89%, an accuracy validation of 75.93%, and a testing accuracy of 84.84%—also, the result of 2638.63 s for the training time consumed with CPU usage. The model evaluation results show that our model has a smaller EER value; 2.583, with FAR = 0.333 and FRR = 4.833. Although the results of our proposed model are better than basic CNN, it is still low and overfitted. It has to be enhanced by better pre-processing steps using another augmentation method required to improve dataset quality.

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Offline Chinese Signature Verification Based on AlexNet

Cited by 9 publications

References 3 publications

Multilingual Offline Signature Verification Based on Improved Inverse Discriminator Network

Multilingual Offline Signature Verification Based on Improved Inverse Discriminator Network

Signature Verification Using Siamese Neural Network One-Shot Learning

CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification

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