Online Signature Verification Method Based on the Acceleration Signals of Handwriting Samples

Bunke, Horst; Csirik, János; Gingl, Zoltán; Griechisch, Erika

doi:10.1007/978-3-642-25085-9_59

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Many types of digitizing devices exist, including gloves, mobile devices, camera-based devices and dedicated signature tablets [102][103][104][105][106][107][108][109]. Every digitizing mode has its own particularities and a different set of recorded measurements.…”

Section: Hardware-related Conditionsmentioning

confidence: 99%

Dynamic signatures: A review of dynamic feature variation and forensic methodology

Linden

Marquis

Bozza

et al. 2018

Forensic Science International

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This article focuses on dynamic signatures and their features. It provides a detailed and critical review of dynamic feature variations and circumstantial parameters affecting dynamic signatures. The state of the art summarizes available knowledge, meant to assist the forensic practitioner in cases presenting extraordinary writing conditions. The studied parameters include hardware-related issues, aging and the influence of time, as well as physical and mental states of the writer. Some parameters, such as drug and alcohol abuse or medication, have very strong effects on handwriting and signature dynamics. Other conditions such as the writer's posture and fatigue have been found to affect feature variation less severely. The need for further research about the influence of these parameters, as well as handwriting dynamics in general is highlighted. These factors are relevant to the examiner in the assessment of the probative value of the reported features. Additionally, methodology for forensic examination of dynamic signatures is discussed. Available methodology and procedures are reviewed, while pointing out major technical and methodological advances in the field of forensic handwriting examination. The need for sharing the best practice manuals, standard operating procedures and methodologies to favor further progress is accentuated.

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Section: Hardware-related Conditionsmentioning

confidence: 99%

Dynamic signatures: A review of dynamic feature variation and forensic methodology

Linden

Marquis

Bozza

et al. 2018

Forensic Science International

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“…Recently different types of WACOM tablets [8]- [10] and Anoto pens [11] appeared in several studies. Some other pens with sensors embedded [12] or attached [13], [14] have been developed as well. These special pens were developed for research purposes, hence usually only a few prototypes exist.…”

Section: Related Workmentioning

confidence: 99%

Online Signature Verification Based on Kolmogorov-Smirnov Distribution Distance

Griechisch

Malik

Liwicki

2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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Online signature verification methods examine the dynamics of the handwriting process to decide whether a signature is probably genuine or forged. Most of the previously proposed methods for online signature verification apply Neural Networks, Dynamic Time Warping, or Hidden Markov Model for classification and they consider several aspects, like planar coordinates, pressure, velocity, and acceleration with respect to time. Here we apply a non-parametric statistical test for a comparison of features and the verification of signatures.

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“…Bunke et al . employed DTW to find the dissimilarity of acceleration signals, which are extracted from signatures obtained from a special pen device. Although the performance is relatively good as compared with that of previous DTW studies, it has a drawback of the high computational overhead.…”

Section: Previous Workmentioning

confidence: 99%

Score level fusion approach in dynamic signature verification based on hybrid wavelet-Fourier transform

Khoh

Ong

Pang

et al. 2013

Security Comm. Networks

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In this work, a dynamic handwritten signature verification system based on hybrid discrete wavelet transform and discrete Fourier transform is presented. Wavelet transform is adopted to analyze the signature information in a multi‐resolution representation while retaining its local information by decomposing the signature into different sub‐bands. Fourier transform is performed to extract the feature descriptor of the decomposed sub‐bands. A dissimilarity score of the extracted features between the test signature and reference data is computed using Euclidean distance and enveloped Euclidean distance. Apart from that, several score level fusion mechanisms have been investigated to combine decisive information to boost up the system performance. The k‐nearest neighbor and support vector machine are applied in order to fuse multiple features. The resulting score value is then normalized and compared with a threshold value in order to decide whether a given signature is genuine or forgery. Experiments are conducted on a released version of benchmark SVC2004 database. Two datasets (Task 1 and Task 2), which have different types of signature information, are used to evaluate the proposed system, and promising verification results are achieved. Copyright © 2013 John Wiley & Sons, Ltd.

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Online Signature Verification Method Based on the Acceleration Signals of Handwriting Samples

Cited by 9 publications

References 14 publications

Dynamic signatures: A review of dynamic feature variation and forensic methodology

Dynamic signatures: A review of dynamic feature variation and forensic methodology

Online Signature Verification Based on Kolmogorov-Smirnov Distribution Distance

Score level fusion approach in dynamic signature verification based on hybrid wavelet-Fourier transform

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