Writer-independent online signature verification based on 2D representation of time series data using triplet supervised network

Xie, Liyang; Zhang, Wu; Xian, Zhang; Li, Yong; Wang, Xinkuang

doi:10.1016/j.measurement.2022.111312

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Writer-independent Online Signature Verification Based On 2-...1

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Cited by 12 publications

(2 citation statements)

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“…The time series data collected from the Wacom tablet is converted into image data of a certain size (499 * 227), showing the features of the spatial trajectory and preserving the main dynamic information. [5].…”

Section: Writer-independent Online Signature Verification Based On 2-...mentioning

confidence: 99%

A Survey on Signature Verification and Forgery Detection System

Sudhan¹,

Sarkar²,

Viswanath³

et al. 2023

Int. J. Res. Publ. Rev.

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Section: Writer-independent Online Signature Verification Based On 2-...mentioning

confidence: 99%

A Survey on Signature Verification and Forgery Detection System

Sudhan¹,

Sarkar²,

Viswanath³

et al. 2023

Int. J. Res. Publ. Rev.

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“…Artificial intelligence (AI) algorithms are used in handwriting signature verification to improve accuracy and reduce workload in the verification process 11 . The artificial neural network (ANN) approach is mostly used in signature verification systems 12 .…”

Section: Introductionmentioning

confidence: 99%

Handwriting identification and verification using artificial intelligence-assisted textural features

Zhao,

2023

Sci Rep

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Intelligent process control and automation systems require verification authentication through digital or handwritten signatures. Digital copies of handwritten signatures have different pixel intensities and spatial variations due to the factors of the surface, writing object, etc. On the verge of this fluctuating drawback for control systems, this manuscript introduces a Spatial Variation-dependent Verification (SVV) scheme using textural features (TF). The handwritten and digital signatures are first verified for their pixel intensities for identification point detection. This identification point varies with the signature’s pattern, region, and texture. The identified point is spatially mapped with the digital signature for verifying the textural feature matching. The textural features are extracted between two successive identification points to prevent cumulative false positives. A convolution neural network aids this process for layered analysis. The first layer is responsible for generating new identification points, and the second layer is responsible for selecting the maximum matching feature for varying intensity. This is non-recurrent for the different textures exhibited as the false factor cuts down the iterated verification. Therefore, the maximum matching features are used for verifying the signatures without high false positives. The proposed scheme’s performance is verified using accuracy, precision, texture detection, false positives, and verification time.

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