Improving online signature verification by user-specific likelihood ratio score normalization

Boutellaa, Elhocine; Bengherabi, Messaoud; Harizi, Farid

doi:10.1109/wosspa.2013.6602379

Cited by 5 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2002 it decreased to 2% [3]. The best result in 2003 was 2.78% [4], in 2008-3.3% [5], in 2012-1% [6], in 2013-2.8% [7]. The above results are illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 80%

“…A lot of methods of person identification by handwritten passwords exist as well as those that analyze dynamics of signature reproducing or a static image of signature [2][3][4][5][6][7][8]10,11]. However not one of them allows us to take into consideration the influence of a signer's psychophysiological state on the results of the identification.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Personal Identification and the Assessment of the Psychophysiological State While Writing a Signature

2015

View full text Add to dashboard Cite

This article discusses the problem of user identification and psychophysiological state assessment while writing a signature using a graphics tablet. The solution of the problem includes the creation of templates containing handwriting signature features simultaneously with the hidden registration of physiological parameters of a person being tested. Heart rate variability description in the different time points is used as a physiological parameter. As a result, a signature template is automatically generated for psychophysiological states of an identified person. The problem of user identification and psychophysiological state assessment is solved depending on the registered value of a physiological parameter.

show abstract

“…In 2002 it decreased to 2% [3]. The best result in 2003 was 2.78% [4], in 2008-3.3% [5], in 2012-1% [6], in 2013-2.8% [7]. The above results are illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Personal Identification and the Assessment of the Psychophysiological State While Writing a Signature

2015

View full text Add to dashboard Cite

show abstract

“…On-line systems use this data captured throughout acquisition. These dynamic characteristics square measures specific to every individual and sufficiently stable similarly as repetitive [3,4,5]. Off-line information may be a 2-D image of the signature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Argones Rua, Enrique, and José Luis Alba socialist [1] and Rodríguez-Serrano, José A., associate degreed Florent Perronnin [3] bestowed an approach exploitation the hidden Markov models (HMMs) in 2 completely different modes: user-specific HMM (US-HMM) and user-adapted universal background models (UBMs) (UA-UBMs) and Comparisons to alternative progressive systems, from the ESRA 2011 signature analysis contest, also are rumored. Tian, Wei, and Jingyuan cardinal [2], Nemmour, Hassiba, and Youcef Chibani [4], Shah, Vaibhav, Umang Sanghavi, and Udit monarch [5], Pushpalatha, K. N., A. K. Gautam, and K. B. Kumar [5] propose offline schemes for signature verification with the algorithmic program for affine registration of true and false signatures 2nd purpose sets, artificial immune system's pertinency for written signature verification and form based mostly geometric options and additional significantly focuses on the gap based parameters like the continuity of the signature textural options square measure computed and concatenated with coefficients of contourlet remodel to make the ultimate feature vector severally for the verification system.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Online Signature Verification System on Mobile Devices for Effective and Secure Biometric

Singh¹,

Kaur²

2017

EIJSE

View full text Add to dashboard Cite

Handwritten signature is that the most generally accepted biometric to biometric identification. The projected on-line written signature verification system consists principally of 3 phases: Signal preprocessing, feature extraction, and have matching. Steps for confirming on-line written signature during this system begin with extracting dynamic knowledge (x and y positions) of points that forming the signature. Pen-movement angles and speed square measure then derived from pen position knowledge. To scale back variations in pen-position and pen-movement angles spatial property, knowledge is normalized. Options of the signature will be extracted victimization projected feature extraction methodology. Such as each signature a novel feature are extracted and this can be amount victimization quantization step size vector. To verify the check signature Manhattan distance (score) are taken between the check signature and therefore the reference signature. If score is a smaller amount than the predefined threshold then the check signature same is claimed to be real signature and if score is over the predefined threshold then it's said to be cast signature.

show abstract

Insider threats to information security: Problem areas in neutralization

Epifantsev

Zhumazhanova

Ложников

2016

2016 17th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM)

View full text Add to dashboard Cite

Improving online signature verification by user-specific likelihood ratio score normalization

Cited by 5 publications

References 13 publications

Personal Identification and the Assessment of the Psychophysiological State While Writing a Signature

Personal Identification and the Assessment of the Psychophysiological State While Writing a Signature

Online Signature Verification System on Mobile Devices for Effective and Secure Biometric

Insider threats to information security: Problem areas in neutralization

Contact Info

Product

Resources

About