Convolutional Neural Network Based Offline Signature Verification Application

Yapıcı, Muhammed Mutlu; Tekerek, Adem; Topaloğlu, Nurettin

doi:10.1109/ibigdelft.2018.8625290

Cited by 15 publications

(13 citation statements)

References 51 publications

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“…Later, some preparation operations such as data pre-processing and data augmentation will be carried require a large amount of data to be trained on. By using a similarity function, SNNs can be trained with less data and still be effective in classifying new types of data [6].…”

Section: Methodsmentioning

confidence: 99%

A Siamese Network based Writer Independent Offline Signature Verification

2023

ijfans

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Signatures are frequently used for both personal authorization and verification. The signatures on numerous papers, including legal agreements and bank checks, must be verified. Offline signature verification is a type of authentication that examines the physical action of signing while measuring the characteristics of an user's handwriting. In the present paper, an offline signature verification method was developed utilising Convolutional Siamese Network of Deep Learning [4]. Convolutional Siamese networks, which are dual networks that shares parameters, can be developed to acquire a feature map. Thus, Convolutional Siamese network is employed to verify the person's signature to a input signature that is saved in the database. Research has been conducted with a dataset of signatures that includes 750 signatures from 30 different individuals. An Accuracy of 91.6% was attained by the proposed solution.

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Section: Methodsmentioning

confidence: 99%

A Siamese Network based Writer Independent Offline Signature Verification

2023

ijfans

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“…Berkay et al [13] proposed a hybrid user-dependent/independent offline signature verification technique with a twochannel CNN for both verification and feature extraction. Muhammed Mutlu et al [14] proposed a Deep Learning (DL)-based offline signature verification method to prevent signature forgery by malicious actors. The DL method used in the study is a CNN.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images

Ozkan,

Erdogmus

2024

Symmetry

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While there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and contracts. In this study, the aim was to design network architectures that work very fast in areas that require only signature images. For this purpose, a new Si-CNN network architecture with existing layers was designed. Afterwards, a new loss function and layer (Si-CL), a novel architecture using Si-CL as classification layer in Si-CNN to increase the performance of this architecture, was designed. This architecture was called Si-CNN+NC (New Classification). Si-CNN and Si-CNN+NC were trained with two datasets. The first dataset which was used for training is the “C-Signatures” (Classification Signatures) dataset, which was created to test these networks. The second dataset is the “Cedar” dataset, which is a benchmark dataset. The number of classes and sample numbers in the two datasets are symmetrical with each other. To compare the performance of the trained networks, four of the most well-known pre-trained networks, GoogleNet, DenseNet201, Inceptionv3, and ResNet50, were also trained with the two datasets with transfer learning. The findings of the study showed that the proposed network models can learn features from two different handwritten signature images and achieve higher accuracy than other benchmark models. The test success of the trained networks showed that the Si-CNN+NC network outperforms the others, in terms of both accuracy and speed. Finally, Si-CNN and Si-CNN+NC networks were trained with the gold standard dataset MNIST and showed superior performance. Due to its superior performance, Si-CNN and Si-CNN+NC can be used by signature experts as an aid in a variety of applications, including criminal detection and forgery.

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“…This is the most accurate learning model for computer vision tasks such as object identification, categorization, and recognition. CNN consists of three block layers: convolutional, pooling, and fully connected [7], [18], [40].…”

Section: ) Cnn: One Of the Deep Learning Models Convolutionalmentioning

confidence: 99%

“…Signature forgery is the fraudulent copying of another person's signature for a particular purpose. Due to every person's different nature and personality, every signature has a different pattern and shape, but human senses have limitations in the verification process if the patterns compared are very similar [7], [8]. This condition can result in a missverified signature strikingly similar to the genuine one.…”

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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Convolutional Neural Network Based Offline Signature Verification Application

Cited by 15 publications

References 51 publications

A Siamese Network based Writer Independent Offline Signature Verification

A Siamese Network based Writer Independent Offline Signature Verification

Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images

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

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