Signature alignment based on GMM for on-line signature verification

Xia, Xing‐Hua; Chen, Zhili; Luan, Fangjun; Song, Xiaoyu

doi:10.1016/j.patcog.2016.12.019

Cited by 30 publications

(9 citation statements)

References 46 publications

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“…Signature attributes (speed, acceleration, direction of movement of the pen, pressure) are represented as time series in a functional approach. Comparison is performed using measures of elastic distance, for example, dynamic transformation of the time scale [17][18][19].…”

Section: A Handwritten Signature Authenticationmentioning

confidence: 99%

Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

Bardamova

Konev

Hodashinsky

et al. 2019

JCOMSS

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Handwritten signatures are used in authentication systems as a universal biometric identifier. Signature authenticity verification requires building and training a classifier. This paper describes a new approach to the verification of handwritten signatures by dynamic characteristics with a fuzzy rule-based classifier. It is suggested to use the metaheuristic Gravitational Search Algorithm for the selection of the relevant features and tuning fuzzy rule parameters. The efficiency of the approach was tested with an original dataset; the type II errors in finding the signature authenticity did not exceed 0.5% for the worst model and 0.08% for the best model.

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Section: A Handwritten Signature Authenticationmentioning

confidence: 99%

Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

Bardamova

Konev

Hodashinsky

et al. 2019

JCOMSS

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show abstract

“…Pixel-scale alignment of offline handwriting has received some attention, albeit less than other alignment problems and mostly for rigid or affine transformations. Alignment has long been significant for online signature matching [1]. In other contexts such as word spotting, pixel-scale alignment has been treated more as a means to an end than as a goal in its own right.…”

Section: A Related Workmentioning

confidence: 99%

“…A slight bias towards likely pairings can help the solution to converge quickly, but overcommitment at this stage can also lead to suboptimal results. The proposed initialization strategy starts with plausible relative probabilities for matching at each of the opposite model's keypoints, and expands this to a full 2D probability distribution via the following process: (1) interpolate squared log probabilities between keypoints on the handwriting skeleton; (2) use a generalized distance transform (GDT) [12] to extend to all other points by adding their squared distance from the skeleton as a penalty; (3) normalize the entire 2D probability distribution to sum to 1.…”

Section: A Measurementmentioning

confidence: 99%

Symmetric Inkball Alignment with Loopy Models

Howe

Chung

2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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Alignment tasks generally seek to establish a spatial correspondence between two versions of a text, for example between a set of manuscript images and their transcript. This paper examines a different form of alignment problem, namely pixel-scale alignment between two renditions of a handwritten word or phrase. Using loopy inkball graph models, the proposed technique finds spatial correspondences between two text images such that similar parts map to each other. The method has applications to word spotting and signature verification, and can provide analytical tools for the study of handwriting variation.

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“…Usually, matching procedures or special function parameter calculations are a need between signatures, requiring more time and space. The common approaches include Dynamic Time Wrapping (DTW) [13], its improved version [14,15,16,17,18,19] and the hidden Markov model (HMM) [20,21].…”

Section: Introductionmentioning

confidence: 99%

Online Signature Verification Based on a Single Template via Elastic Curve Matching

Hua-cheng

Zheng

Zhan

et al. 2019

Sensors

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Person verification using online handwritten signatures is one of the most widely researched behavior-biometrics. Many signature verification systems typically require five, ten, or even more signatures for an enrolled user to provide an accurate verification of the claimed identity. To mitigate this drawback, this paper proposes a new elastic curve matching using only one reference signature, which we have named the curve similarity model (CSM). In the CSM, we give a new definition of curve similarity and its calculation method. We use evolutionary computation (EC) to search for the optimal matching between two curves under different similarity transformations, so as to obtain the similarity distance between two curves. Referring to the geometric similarity property, curve similarity can realize translation, stretching and rotation transformation between curves, thus adapting to the inconsistency of signature size, position and rotation angle in signature curves. In the matching process of signature curves, we design a sectional optimal matching algorithm. On this basis, for each section, we develop a new consistent and discriminative fusion feature extraction for identifying the similarity of signature curves. The experimental results show that our system achieves the same performance with five samples assessed with multiple state-of-the-art automatic signature verifiers and multiple datasets. Furthermore, it suggests that our system, with a single reference signature, is capable of achieving a similar performance to other systems with up to five signatures trained.

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Signature alignment based on GMM for on-line signature verification

Cited by 30 publications

References 46 publications

Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

Symmetric Inkball Alignment with Loopy Models

Online Signature Verification Based on a Single Template via Elastic Curve Matching

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