On-Line Signature Verification Based on Logarithmic Spectrum

Wu, Quen-Zong; Lee, Suh-Yin; Jou, I-Chang

doi:10.1016/s0031-3203(98)00058-2

Cited by 30 publications

(13 citation statements)

References 9 publications

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“…Other parameters are numerically derived from time functions representative of a signature, like, for instance, the average (AVE), the root mean square (rms), and the maximum (MAX) and minimum (MIN) values of position, displacement, speed, and acceleration [169], [170], [227]. In other cases, the parameters-that have been used for both dynamic and static signature verification-are determined as coefficients obtained from mathematical tools as Fourier [41], [54], [56], [57], [59], [194], [268], [345], [347], Hadamard [228], cosine [193], wavelet [49], [75], [76], [176], [189], [194], [195], [220], [274], [323], [332], [356], Radom [38], and fractal [127], [206] transforms.…”

Section: Feature Extractionmentioning

confidence: 99%

“…In order to avoid deformation of reference signatures when matched against test specimens, a well-suited form of asymmetric DTW was defined [186], [187], [189]. Other template matching approaches can use well-defined distortion measures [344], similarity measures [347], displacement functions [199], [200], relaxation matching [128], accumulated position and velocity distances based on split-and-merge mechanisms [346], fuzzy logic [185], and pattern matching [283], [318].…”

Section: Classificationmentioning

confidence: 99%

“…GAs were used to determine the optimal personalized features for each subject whereas verification decision was achieved by fuzzy logic. Fourier analysis was used by Wu et al [347] for online signature verification. In particular, cepstrum coefficients were extracted and used for verification, according to a dynamic similarity measure.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

538

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: Feature Extractionmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

538

295

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“…Significant research has been conducted in feature extraction and selection for the application of on-line signature verification (Plamondon, 1994;Plamondon and Leclerc, 1989;Brault and Plamondon, 1993;Sebastian et al, 2003;Huang and Yan, 1995;Wirtz, 1995;Wu et al, 1998;Feng and Wah, 2003;Lee et al, 2004;Lei and Govindaraju, 2004). Digitizing devices capture dynamic information of the pen trajectory (X-, Ycoordinates, pressure, altitude, etc.).…”

Section: Introductionmentioning

confidence: 99%

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

Lei

Govindaraju

2005

Pattern Recognition Letters

131

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“…This section only concentrates on the techniques that used transform domain. Transform domain approaches include Fourier descriptors-based approaches [68,108,116,123,130,139], Discrete Wavelet Transform approaches [74,76,109,110,117,125,127,130,160,183,184,185], Zernike moments [57], Linear Prediction Coding [40,41], Mellin transform [100], Discrete Cosine Transform [47,88,144], Gabor [91,92], Autoregressive [97,103,153] and Linear Programming Descriptor [47]. Fourier descriptors are invariant with respect to translation, rotation and magnification of the trace.…”

Section: Transform Domain 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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On-Line Signature Verification Based on Logarithmic Spectrum

Cited by 30 publications

References 9 publications

Automatic Signature Verification: The State of the Art

Automatic Signature Verification: The State of the Art

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

Online Signature Verification: State of the art

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