A Study on Enhanced Dynamic Signature Verification for the Embedded System

Kim, Jin Whan; Cho, Hyuk Gyu; Young, Eui

doi:10.1007/11565123_42

Cited by 2 publications

(1 citation statement)

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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%

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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