GloveSignature: A Virtual-Reality-Based System for Dynamic Signature Verification

Tolba, A. S.

doi:10.1006/dspr.1999.0351

Cited by 10 publications

(6 citation statements)

References 33 publications

(29 reference statements)

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“…This approach can be the simplest way for a user to interact with the computer by using handwriting, and its potential has been specifically demonstrated in the domain of automatic signature verification [207], [209], [211]. In addition, a handglove device for virtual reality applications has been used for online signature verification [317]. This device can provide data on both the dynamics of the pen motion during signing and the individual's hand shape.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…Therefore, the smaller the feature vector, the greater the number of individuals that can be enrolled in the system and the faster speeds that can be achieved in the verification process [77], [78]. In recent years, several techniques have been proposed for feature selection based on principal component analysis (PCA) and self-organizing feature maps [317], sequential forward search/sequential backward search (SFS/SBS) [80], inter-intra class distance radios (ICDRs) [82], and analysis of feature variability [227], [252]. Forgerybased feature analysis has also been proposed to select feature sets well suited for random and skilled forgery, respectively.…”

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

540

295

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Abstract-In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

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Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

540

295

View full text Add to dashboard Cite

show abstract

“…Both local and global task primitives are referred and processed for the system evaluation sheet and the decision are taken about the authenticity of the signature of the system . The global performance of the system is compared and measured using two different classifiers [5].…”

Section: B a Hybrid On/off Line Handwritten Signature Verification Smentioning

confidence: 99%

“…Using histograms for online signature verification was first suggested by Nelson etal [4]. They have also been used as part of the feature set in [1] and [5]. However, in [1] and [9], the use of histograms is limited only to angles obtain from vectors connecting two consecutive points.…”

Section: Online Signature Verification Algorithmmentioning

confidence: 99%

Authentication System for Any Touch Devices Using DWT

Goslar¹,

Madankar²

2016

IJECS

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Abstract-In today's world, the identification techniques based on human biometric features gaining much more importance. For e.g. Iris recognition system, face recognition system, finger print recognition system, retina scanning system, etc. Some important area of other techniques is also to recognize the human hand signature. Also, there is the requirement of some techniques which works automatically based on some standard computer algorithms for recognition. For e.g. to identify the criminal identity automatically based on biometric features. The aim of research is to develop an on-line system for signature verification using Discrete Wavelet Transform (DWT). Discrete Wavelet Transform (DWT) algorithm is used for features extraction. In online signature dynamic features are usually available. Genuine or Forgery signature is substantiating based features comparison. Each feature of an input signature is extracted using various steps discrete wavelet transform algorithm are matched against a reference signature to identify the genuine signature. The final decision of verification is made based on the matched features of both the signatures.

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“…Instrumented data gloves furnished with sensors for detecting finger bend, hand position, and orientation for detecting hand signatures are used in handwritten verification [13]. A method for automatic handwritten signature verification that depends on global features that summarize different aspects of signature shape and dynamics of signature production is studied in [14].…”

Section: Introductionmentioning

confidence: 99%

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

Daqrouq

Sweidan

Balamesh

et al. 2017

Entropy

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Abstract:Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%.

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GloveSignature: A Virtual-Reality-Based System for Dynamic Signature Verification

Cited by 10 publications

References 33 publications

Automatic Signature Verification: The State of the Art

Automatic Signature Verification: The State of the Art

Authentication System for Any Touch Devices Using DWT

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

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