Survey of Preprocessing Techniques and Classification Approaches in Online Signature Verification

Saleem, Mohammad; Kővári, Bence

doi:10.1007/978-3-030-50347-5_23

Cited by 6 publications

(5 citation statements)

References 50 publications

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“…In the verification process, these signatures are utilized for training and testing. In order to provide both training and testing samples, the number of genuine signatures should be large enough [30].…”

Section: Source Of Data and Databasesmentioning

confidence: 99%

A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques

Hashim

Ahmed

Alkhayyat

2022

Scientific Programming

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Nowadays, the verification of handwritten signatures has become an effective research field in computer vision as well as machine learning. Signature verification is naturally formulated as a machine-learning task. This task is performed by determining if the signature is genuine or forged. Therefore, it is considered a two‐class classification issue. Since handwritten signatures are widely used in legal documents and financial transactions, it is important for researchers to select an efficient machine-learning technique for verifying these signatures and to avoid forgeries that may cause many losses to customers. So far, great outcomes have been obtained when using machine learning techniques in terms of equal error rates and calculations. This paper presents a comprehensive review of the latest studies and results in the last 10 years in the field of online and offline handwritten signature verification. More than 20 research papers were used to make a comparison between datasets, feature extraction, and classification techniques used in each system, taking into consideration the problems that occur in each. In addition, the general limitations and advantages of machine-learning techniques that are used to classify or extract signature features were summarized in the form of a table. We also present the general steps of the verification system and a list of the most considerable datasets available in online and offline fields.

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Section: Source Of Data and Databasesmentioning

confidence: 99%

A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques

Hashim

Ahmed

Alkhayyat

2022

Scientific Programming

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“…Generally, online approaches are based on dynamic characteristics such as azimuth, tip pressure, and altitude. Numerous digitizing devices (e.g., tablets, smartwatches, and IMU-equipped pens) have been used to capture stylus or wrist motion data in previous research [ 3 , 4 , 8 , 12 , 13 , 14 ]. Recently, device-free approaches have drawn the attention of researchers.…”

Section: Related Workmentioning

confidence: 99%

“…Prior research [ 8 ] seized the opportunity of the sensing capability in popular wearable devices, using a a smartwatch that captures the motion of the wrist via HSV IMU sensors. Compared to other research [ 3 , 4 , 5 , 6 ], this novel approach solves hardware inaccessibility deficiencies. However, it also has the following weaknesses.…”

Section: Introductionmentioning

confidence: 96%

“…This fuels the growth of HSV research. According to the signature collection approach, HSV schemes can be classified as online verification and offline verification [ 3 ]. Offline verification denotes signing the person’s name on paper materials and verifying the signature afterward.…”

Section: Introductionmentioning

confidence: 99%

“…Within the scope of online HSV, a drawing input device, such as a digitizer or touchpad, is used in most previous schemes [ 3 , 4 , 5 , 6 ], in which users can sign their signatures on an electromagnetic resonance technology [ 7 ] plane with a specialized stylus. The limitations of such schemes are that they require specialized hardware and are therefore unsuitable when users must sign on paper-like materials.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Acoustic Sensing Based on Online Handwritten Signature Verification

Chen

Lin

Zou

et al. 2022

Sensors

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Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user’s signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2% AUC and 1.25% EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc.

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MMHSV: A Multimodal Handwritten Signature Verification Fusing Dynamic and Static Feature

Li,

Wang,

Jin

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Survey of Preprocessing Techniques and Classification Approaches in Online Signature Verification

Cited by 6 publications

References 50 publications

A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques

A Comparative Study among Handwritten Signature Verification Methods Using Machine Learning Techniques

Acoustic Sensing Based on Online Handwritten Signature Verification

MMHSV: A Multimodal Handwritten Signature Verification Fusing Dynamic and Static Feature

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