On-line signature verification algorithm incorporating pen position, pen pressure and pen inclination trajectories

Sakamoto, Daisuke; Morita, Hayato; Ohishi, T.; Komiya, Yusuke; Matsumoto, Takashi

doi:10.1109/icassp.2001.941084

Cited by 32 publications

(13 citation statements)

References 2 publications

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“…, N is the discrete time index given by the acquisition device and N is the time duration of the signature in sampling units. Although pen inclination trajectories have shown some discriminative capabilities in other works (Hangai et al, 2000;Sakamoto et al, 2001;Pacut and Czajka, 2001), the use of these two functions in our system degrades the verification performance (as it is shown in Section 4.4.1). As a result, the basic function set consists of x n , y n and p n .…”

Section: Basic Functionsmentioning

confidence: 84%

HMM-based on-line signature verification: Feature extraction and signature modeling

Fiérrez-Aguilar

Ortega‐Garcia

Ramos

et al. 2007

Pattern Recognition Letters

280

118

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Section: Basic Functionsmentioning

confidence: 84%

HMM-based on-line signature verification: Feature extraction and signature modeling

Fiérrez-Aguilar

Ortega‐Garcia

Ramos

et al. 2007

Pattern Recognition Letters

280

118

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“…Such a modular approach allows for coverage of a wide span of input methods and technologies starting with traditional mouse-based input and its enhancements with gesture dynamics [2], going through classic 2-DOF digitizers, tablets, and sign pads for 2D graphical input and their enhanced multiple-DOF versions capable to track the stylus azimuth, elevation, and applied forces [3]. We also cover current touch screen based input both for singleand multi-touch surfaces [4].…”

Section: Framework Building Blocks and Componentsmentioning

confidence: 99%

A human computer interactions framework for biometric user identification

Kanev

Maria

Bottoni

2016

Proc. 14th Int. Conf. On Global Research and Education, Inter-Academia 2015

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Computer assisted functionalities and services have saturated our world becoming such an integral part of our daily activities that we hardly notice them. In this study we are focusing on enhancements in Human-Computer Interaction (HCI) that can be achieved by natural user recognition embedded in the employed interaction models. Natural identification among humans is mostly based on biometric characteristics representing what-we-are (face, body outlook, voice, etc.) and how-we-behave (gait, gestures, posture, etc.) Following this observation, we investigate different approaches and methods for adapting existing biometric identification methods and technologies to the needs of evolving natural human computer interfaces.

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“…The Majority Classifier (MC) accepts a test signature as a genuine one if the number of feature values which pass a predetermined test is larger than half the total number of tested features. Function-based approach treats the signing process as a set of one-dimensional discrete time functions and performs the matching process based on an elastic measure such as DTW [7,9,14,15,17,18,31,36,44,45,51,54,67,71,72,79,81,84,85,86,93,96,98,99,101,104,106,112,122,124,126,129,140,142,143,146,163,167,200,221,222] or HMM [2,5,21,29,30,32,58,…”

Section: Literature Reviewmentioning

confidence: 99%

“…Full Signal Warping [14,15,51,72,79,81,85,176,212], Extreme Points Warping [17,44,45], Shape contexts DTW [78,83,194], Continuous DTW [67,71,86,99,101,114,167,214], Mahalanobis-based DTW [45,106,140,158], PCA, NCA and MCA based DTW [18,146,226], Bidirectional backward-merging DTW [147], GA and PA-based DTW [121,143].…”

Section: Dynamic Time Warping (Elastic Matching)mentioning

confidence: 99%

Online Signature Verification: State of the art

El-Henawy¹,

Rashad²,

Nomir³

et al. 2005

IJCT

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ABSTRACTonline or Dynamic signature verification (DSV) is one of the most acceptable, intuitive, fast and cost effective tool for user authentication. DSV uses some dynamics like speed, pressure, directions, stroke length and pen-ups/pen-downs to verify the signer's identity. The state of the art in DSV is presented in this paper. several approaches for DSV are compared and the most influential techniques in this field are highlighted. We concentrate on the relationship between the verification approach used (the nature of the classifier) and the type of features that are used to represent the signature. Literature ReviewThe signing process can be described at two levels: at the high level, the signing method is recovered from long-term memory; parameters are then specified such as size, shape, timing; finally, at a peripheral level, commands are generated for the biophysical muscles. So, the signing process is believed to be a reflex action (ballistic action), rather than a deliberate action. Ballistic handwriting is characterized by a spurt of activity, without positional feedback, whereas deliberate handwriting is characterized by a conscious attempt to produce a visual pattern with the aid of positional feedback [25]. The problem arises here is that many signers can produce their signatures both ballistically and deliberately based on the nature and importance of the task. The process of Signature verification is a two-class problem where the input signature is classified as genuine or forged (i.e. belonging to an impostor). The simplest approach to compare two signatures is to compute correlations between the test signature values and the reference signature values. Such point-topoint comparison usually does not work well since there are a large number of variations between the two signatures and the correlation can be affected by the translation, rotation or scaling of the signature . The decision is made by comparing the similarity score between the input and the enrolled signatures with a predetermined threshold. A tablet device is used to capture dynamic information of a signature. Such dynamic information is a set of sequences of sampled points over time. Each sequence depicts an action performed during the signing process. Signatures from the same person may have different trajectory lengths due to local stretching, compression, omission or additional parts, and hence they are represented by feature vectors of differing lengths. Therefore, straight forward methods, such as the Euclidian distance or autocorrelation, are not very useful in calculation of the dissimilarity value between two signatures [6]. Such point-to-point comparison usually does not work because some portions of any two genuine signatures of the same person may vary significantly. To overcome the problem, non-linearly approaches such as Dynamic Time Warping (DTW) algorithm and Hidden Markov Models (HMM) are commonly used in aligning two signatures. Plamondon and Lorette [141] categorized the various signature verification methods int...

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On-line signature verification algorithm incorporating pen position, pen pressure and pen inclination trajectories

Cited by 32 publications

References 2 publications

HMM-based on-line signature verification: Feature extraction and signature modeling

HMM-based on-line signature verification: Feature extraction and signature modeling

A human computer interactions framework for biometric user identification

Online Signature Verification: State of the art

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