A comparative study on the consistency of features in on-line signature verification

Lei, Hansheng; Govindaraju, Venu

doi:10.1016/j.patrec.2005.05.005

Cited by 133 publications

(73 citation statements)

References 15 publications

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“…Some studies focus on the best selection of the features, for example Lei & Govindaraju, (2005). In this paper they compared the discriminative power of the biometric features.…”

Section: Dynamic Signature Verification: a Form Of Biometric Authentimentioning

confidence: 99%

Haptics and the Biometric Authentication Challenge

Kanneh¹,

Sakr²

2010

Advances in Haptics

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“…Some studies focus on the best selection of the features, for example Lei & Govindaraju, (2005). In this paper they compared the discriminative power of the biometric features.…”

Section: Dynamic Signature Verification: a Form Of Biometric Authentimentioning

confidence: 99%

Haptics and the Biometric Authentication Challenge

Kanneh¹,

Sakr²

2010

Advances in Haptics

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“…Thus, information about pen position, pen pressure, pen altitude, and pen azimuth is available 1 Researchers who use these databases generally use all of the information in the database [9]- [14]. However, it has argued that using the pressure, azimuth, and altitude can cause instability and deteriorate the performance [8][15]- [17].…”

Section: Introductionmentioning

confidence: 99%

“…The results of Task 1 outperformed those of Task 2 [8]. As a result, some researchers have stopped using pen pressure, [15], pen altitude, and pen azimuth [16] or have expressed concern about using pen inclination features [17]. On the other hand, some researchers have proposed algorithms taking account of pen inclination features and reported improved performance [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification

Muramatsu

Matsumoto

Lecture Notes in Computer Science

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Abstract. Many algorithms for online signature verification using multiple features have been proposed. Recently it has been argued that pen pressure, azimuth, and altitude can cause instability and deteriorate the performance. Algorithms without pen pressure and inclination features outperformed with them in SVC2004. However, we previously found that these features improved the performance in evaluations using our private database. The effectiveness of the features thus depended on the algorithm. Therefore, we re-evaluated our algorithm using the same database as used in SVC2004 and discuss the effectiveness of pen pressure, azimuth and altitude. Experimental results show that even though these features are not so effective when they are used by themselves, they improved the performance when used in combination with other features. When pen pressure and inclination features were considered, an EER of 3.61% was achieved, compared to an EER of 5.79% when these features were not used.

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“…User recognition by his or her signature can be divided into static (offline), where the signature written on paper is digitized, and dynamic, where users write their signature in a digitizing tablet, tablet-personal computer, personal digital assistant (PDA) stylus [2] or similar; the information acquired depends on the input device. Taking into consideration the highest security ranks that can be attained by dynamic systems, most of the efforts of the international scientific community are concentrated on this type [3,4]. Static systems are limited to use in legal-related cases [5].…”

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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A comparative study on the consistency of features in on-line signature verification

Cited by 133 publications

References 15 publications

Haptics and the Biometric Authentication Challenge

Haptics and the Biometric Authentication Challenge

Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification

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

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