Machine learning-based offline signature verification systems: A systematic review

Hameed, M. Muzaffar; Ahmad, Rodina; Kiah, Miss Laiha Mat; Murtaza, Ghulam

doi:10.1016/j.image.2021.116139

Cited by 41 publications

(22 citation statements)

References 61 publications

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“…Therefore, it is difficult to evaluate the quality of signature verification and to compare it with other approaches and systems. However, as shown in [15], existing SNNs demonstrate superiority over most competitors.…”

Section: Signature Verificationmentioning

confidence: 99%

A Survey on Symmetrical Neural Network Architectures and Applications

et al. 2022

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A number of modern techniques for neural network training and recognition enhancement are based on their structures’ symmetry. Such approaches demonstrate impressive results, both for recognition practice, and for understanding of data transformation processes in various feature spaces. This survey examines symmetrical neural network architectures—Siamese and triplet. Among a wide range of tasks having various mathematical formulation areas, especially effective applications of symmetrical neural network architectures are revealed. We systematize and compare different architectures of symmetrical neural networks, identify genetic relationships between significant studies of different authors’ groups, and discuss opportunities to improve the element base of such neural networks. Our survey builds bridges between a large number of isolated studies with significant practical results in the considered area of knowledge, so that the presented survey acquires additional relevance.

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Section: Signature Verificationmentioning

confidence: 99%

A Survey on Symmetrical Neural Network Architectures and Applications

et al. 2022

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“…For other works regarding online information, the author kindly refers the reader to relevant recent surveys and techniques (MOHAMMED et al, 2015;BHATIA, 2016;OLIVEIRA, 2019a;DIAZ et al, 2019;HAMEED et al, 2021).…”

Section: Online Signature Verificationmentioning

confidence: 99%

An Offline Writer-Independent Signature Verification Method with Robustness Against Scalings and Rotations

Pachas

Gastal

2021

2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

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Handwritten signatures are still one of the most used and accepted methods for user authentication. They are used in a wide range of human daily tasks, including applications from banking to legal processes. The signature verification problem consists of verifying whether a given handwritten signature was generated by a particular person, by comparing it (directly or indirectly) to genuine signatures from that person. In this research work, a new offline writer-independent signature verification method is introduced (namedbased on a combination of handcrafted Moving Least-Squares features and features transferred from a convolutional neural network. In our experiments, VerSig-R outperforms state-of-the-art techniques on Western-style signatures (CEDAR dataset), while also obtaining competitive results on South Asian-style handwriting (Bangla and Hindi datasets). Furthermore, a wide range of experiments demonstrate that VerSig-R is the most robust in relation to differences in scale and rotation of the signature images. This work also presents a discussion on dataset bias and on cross-dataset performance of VerSig-R, as well as a small user study showing that the proposed technique outperforms the expected human accuracy on the signature-verification task. Finally, a discussion on the impact of the number of signature examples (per writer) used during training on performance and execution time is presented.

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“…Signature verification plays an essential role in many fields, including economy, judiciary, education, and other vast areas. In the economy, signature verification can help us verify the authenticity of signatures in financial credit [2]. In the judiciary, signature verification could guild us in determining the credibility of case materials.…”

Section: Introductionmentioning

confidence: 99%

Signature Verification by Multi-Size Assembled-Attention with the Backbone of Swin-Transformer

Chu

Zhang

Zheng

2023

Preprint

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Handwritten signature verification is an indispensable means of identification in biometric information recognition and has broad application prospects and research significance in financial, judicial, and educational systems. With the advancement of signature forgery technology, people have higher requirements for the accuracy and efficiency of signature verification, and sophisticated convolutional networks capable of automatic feature extraction are gradually being applied to the field of handwriting recognition. However, these convolution methods still have the potential for improvement in recognition capability, generalization capability, and accuracy rate. This paper proposes a novel network model, Multi-Size Assembled-Attention Swin-Transformer network, to perform signature handwriting authenticity identification. The inputs to the network are signature images that are resized to multiple sizes, including (224, 224), (112, 112), (56, 56). Then, features within the same image are extracted using the self-attention mechanism in Swin-Transformer, and features between different images are also extracted with the cross-attention mechanism in Assembled-Attention Block, enabling signature feature information to interact within the same image and between different images. Also, Regularized Dropout strategy and adversarial method are implemented in the training stage. Therefore, our method considerably prompts the identification ability of the signature handwriting and obtains state-of-the-art performance, especially 57.1% and 50.4% improvement, in the situation of training in CEDAR and evaluation in Bengali and Hindi. Meantime, we evaluated the impact of the input images passing through the model's times on performance and found that the network achieves the optimal performance at times of four.

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Machine learning-based offline signature verification systems: A systematic review

Cited by 41 publications

References 61 publications

A Survey on Symmetrical Neural Network Architectures and Applications

A Survey on Symmetrical Neural Network Architectures and Applications

An Offline Writer-Independent Signature Verification Method with Robustness Against Scalings and Rotations

Signature Verification by Multi-Size Assembled-Attention with the Backbone of Swin-Transformer

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