Dynamic handwritten Chinese signature verification

Chang, Hong-De; Wang, J.-F.; Suen, H.-M.

doi:10.1109/icdar.1993.395736

Cited by 18 publications

(10 citation statements)

References 6 publications

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“…Table IV shows some of the NN models that have been used recently: Bayesian NNs [30], [351], multilayer perceptrons (MLPs) [7], [15], [17], [126], [167], [345], [350], time-delay NNs [22], [167], ARTMAP NNs [215]- [217], backpropagation neural networks (BPNs) [13], [15], [47], [66]- [68], self-organizing maps [1], [2], and radial basis functions (RBFs) [13], [109], [203], [232], [316]. Fuzzy NN, which combine the advantages of both NNs and fuzzy rule-based systems, has also been considered [102], [270], [353].…”

Section: Classificationmentioning

confidence: 99%

“…Many more differences can be expected when considering signatures written by people from non-Western countries. For this purpose, specific approaches have been proposed in the literature for Chinese [30], [36], [163], [182]- [184], [349] and Japanese [318], [364], [365], [367] signatures, which can consist of independent symbols, as well as Arabian/Persian [28], [29], [47], [134] signatures, which are cursive sketches usually independent of the person's name. In general, as the need for cross-cultural applications increases, it is becoming more and more important to evaluate both the extent to which personal background affects signature characteristics and the accuracy of the verification process.…”

Section: Classificationmentioning

confidence: 99%

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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: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

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

“…In [23], a technique based on Bayesian neural networks is presented for dynamic signature verification of Chinese signatures. A set of 16 features is used: total time, average velocity, number of segments, average length in the eight directions of the signature, width/height ratio, left-part/right-part den-sity ratio, and upper-part/lower-part density ratio.…”

Section: Digitizing Table-based Systemsmentioning

confidence: 99%

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

Tolba

1999

Digital Signal Processing

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“…The most common method to find the similarity between the input feature vector and the stored template is to use a classical distance measure such as Euclidean distance, Mahalanobis distance, Canberra Distance, Euclidean Distance, City Block distance and Hamming Distance. Some of the algorithms that used features-based approaches can be found in [4,9,12,22,28,33,49,52,65,93,94,107,139,161,162,179,193,195,199,200,205,208,209,210,215]. L. Lee et al [12] discussed a number of techniques for feature selection from a set of 42 features and 49 normalized features.…”

Section: Features-based Approachesmentioning

confidence: 99%

“…Julio Romo and Rogelio Silva [209] proposed a DSV system based on creating Optimal Prototype Functions (OPF) of the discriminant features of the signatures. Chang and Hong-De [28] proposed a DSV system of Chinese signatures based on Bayesian neural networks.…”

Section: Features-based Approachesmentioning

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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Dynamic handwritten Chinese signature verification

Cited by 18 publications

References 6 publications

Automatic Signature Verification: The State of the Art

Automatic Signature Verification: The State of the Art

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

Online Signature Verification: State of the art

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